medical case study generator

• AI Case Study Generator

Generate professional and engaging case studies effortlessly with our free AI Case Study creator. Simplify the process and showcase your success.

• Browse all Apps
• Acronym Generator
• Active to Passive Voice Converter
• AI Answer Generator
• AI Essay Generator
• AI Letter Generator
• AI Product Roadmap Generator
• AI Content & Copy Generator
• Amazon Ad Headline Generator
• Amazon Product Bullet Points Generator
• Amazon Product Title Generator
• Article Rewriter
• Author Bio Generator
• Blog Heading Expander
• Blog Outline Generator
• Blog Ideas Generator
• Blog Insights Generator
• Blog Introduction Generator
• Blog Post Generator‍
• Blog Section Writer
• Blog Title Alternatives Generator
• Blog Title Generator
• Bullet Points Generator
• Bullet Points to Paragraph Generator
• AI Case Study Title Generator
• Checklists Ideas Generator
• Compelling Bullet Points Generator
• Conclusion Generator
• Content Expander
• Content Idea Generator
• Content Readability Improver
• Content Tone Changer
• Courses Ideas Generator
• eBay Ad Headline Generator
• eBay Product Bullet Points Generator
• eBay Product Description Generator
• eBay Product Title Generator
• Ebooks Ideas Generator
• Editorial Calendar Generator
• Essay Title Generator
• Etsy Ad Headline Generator
• Etsy Product Bullet Points Generator
• Etsy Product Description Generator
• Etsy Product Title Generator
• First to Third Person Converter
• Flipkart Ad Headline Generator
• Flipkart Product Bullet Points Generator
• Flipkart Product Description Generator
• Flipkart Product Title Generator
• Headline Generator - 100% Free AI Tool
• Headline Intro Generator
• Hook Generator
• Local Services Description Generator
• Negative Keyword List Generator
• Pain Agitate Solution Generator
• Paragraph Generator
• Paragraph Shredder
• Paraphrasing Tool (Paraphraser)
• Passive to Active Voice Converter
• Personal Bio Generator (Profile Bio)
• Poll Question and Answers Generator
• Post and Caption Ideas Generator
• Product or Service Generator
• Product Review Generator
• Sentence Rewriter
• Subheadings Generator
• AI Summarizing Tool: Summary Generator
• Synopsis Generator
• Text Extender
• Video Marketing Strategy Generator
• Add Emoji to Text
• Amazon Product Description Generator
• Baby Name Generator: Find the Unique Baby Names
• Chat Message Reply Writer
• Course Description Generator
• Course Name Generator
• Email Subject Line Generator
• Email Writer & Generator
• Excel Formula Generator
• FAQs Generator
• Fitness Exercise & Workout Generator
• Grammar Checker
• Image Generator
• Restaurant Review Generator
• Song Lyrics Generator
• Story Plot Generator
• Storyteller | Storymaker
• Text Rewriter
• What to do?
• Facebook Bio Generator
• Facebook Group Post Comment Generator
• Facebook Group Post Generator
• Facebook Hashtag Generator
• Facebook Poll Questions Generator
• Facebook Post Comment Generator
• Facebook Post Generator
• Hacker News Post Comment Generator
• Hacker News Post Generator
• Hashtag Generator
• IndieHackers Post Generator
• IndieHackers Post Comment Generator
• Instagram Bio Generator
• Instagram Caption Generator
• Instagram Hashtag Generator
• Instagram Reels Ideas Generator
• Instagram Reel Script Generator
• Instagram Threads Bio Generator
• Instagram (meta) Threads Generator
• LinkedIn Hashtag Generator
• LinkedIn Poll Questions Generator
• Linkedin Summary Generator
• LinkedIn Experience Description Generator
• Linkedin Headline Generator
• LinkedIn Post Comment Generator
• LinkedIn Post Generator
• LinkedIn Recommendation Generator
• Pinterest Bio Generator
• Pinterest Board Name Generator
• Pinterest Description Generator
• Pinterest Hashtag Generator
• Poem Generator
• Quora Answer Generator
• Quora Questions Generator
• Reddit Post Generator
• Social Media Content Calendar Generator
• TikTok Bio Generator
• TikTok Caption Generator
• TikTok Content Ideas Generator
• TikTok Hashtag Generator
• TikTok Script Generator
• TikTok Ads Generator
• Tweet Generator AI Tool
• Tweet Ideas Generator
• Tweet Reply Generator
• Twitter Bio Generator
• Twitter Hashtag Generator
• Twitter Poll Questions Generator
• Whatsapp Campaign Template
• YouTube Channel Name Generator
• Youtube Hashtag Generator
• Youtube Shorts Ideas Generator
• Youtube Shorts Script Generator
• YouTube Tags Generator
• YouTube Title Generator
• YouTube Video Script Generator
• About Us Page Generator
• Advertisement Script Generator
• Advertising Campaign Generator
• AI Email Newsletter Generator
• AI SWOT Analysis Generator
• AIDA Generator
• Before After Bridge Copy Generator
• Buyer Challenges Generator
• Buyer Persona Generator
• Call to Action Generator
• Content Brief Generator
• Content Calendar Generator
• Digital Marketing Strategy Generator
• Elevator Pitch Generator
• Email Campaign Template
• Podcast Episode Title Generator AI Tool
• Facebook Ads Generator
• Feature Advantage Benefit Generator
• Feature to Benefit Converter
• Glossary Generator
• Go To Market Strategy Generator
• Google Ad Headline Generator
• AI Google Ads Copy Generator
• AI Google Sheets Formula Generator
• Identify Popular Questions
• Landing Page and Website Copies Generator
• Lead Magnet Generator
• LinkedIn Ad Generator
• Listicle Generator
• Marketing Plan Generator
• Marketing Segmentation Generator
• AI Podcast Name Generator Tool
• Podcast Questions Generator
• Customer Persona Generator
• Press Release Ideas Generator
• Press Release Quote Generator
• Press release Writer
• Product Launch Checklist Generator
• Product Name Generator
• AI Q&A Generator
• Questions and Answers Generator
• Reply to Reviews and Messages Generator
• Slide Decks Ideas Generator
• Slideshare presentations Ideas Generator
• SMS Campaign Template
• Survey Question Generator
• Talking Points Generator
• Twitter Ads Generator
• Youtube Channel Description Generator
• Youtube Video Description Generator
• Youtube Video Ideas Generator
• Webinar Title Generator
• Webinars Ideas Generator
• Whitepapers Ideas Generator
• YouTube Video Topic Ideas Generator
• Keyword Research Strategies Generator
• Keywords Extractor
• Keywords Generator
• Long Tail Keyword Generator
• Meta Description Generator
• SEO Meta Title Generator
• Sales & Cold Calling Script Generator
• Content Comparison Generator
• AI Follow-Up Email Generator
• Icebreaker Generator
• LinkedIn Connection Request Generator
• LinkedIn Followup Message Generator
• LinkedIn Inmail Generator
• LinkedIn Message Generator
• Pain Point Generator
• Proposal Generator
• Sales Qualifying Questions Generator
• AI Sales Email Generator
• Sales Pitch Deck Generator
• Voice Message Generator
• Closing Ticket Response Writer
• Request for Testimonial Email Generator
• Support Ticket Auto Reply Writer
• AI Support Ticket Explainer
• Support Ticket Reply Writer
• Ticket Resolution Delay Response Writer
• Billing Reminder Email Writer
• Customer Contract Summarizer
• Customer QBR Presentation Writer
• Business Meeting Summary Generator
• Monthly Product Newsletter Writer
• Product Questions for Customer Generator
• Business Name Generator
• Business idea pitch generator
• Business ideas generator
• Company Slogan Generator
• Core Values Generator
• Domain Name Generator
• Event Description generator
• Event ideas generator
• Metaphor Generator
• Micro SaaS ideas Generator
• Project Plan Generator
• Startup ideas Generator
• Vision and Mission Generator
• Product Description Generator AI Tool
• Product Description with Bullet Points Generator
• SMS and Notifications Generator
• Tagline and Headline Generator
• Testimonial and Review Generator
• Value Prop Generator
• Convincing Premium
• Critical Premium
• Funny Premium
• Humble Premium
• Humorous Premium
• Informative Premium
• Inspirational Premium
• Joyful Premium
• Passionate Premium
• Thoughtful Premium
• Urgent Premium
• Worried Premium
• English (US)
• German (Germany)
• Italian (Italy)
• Japanese (Japan)
• Russian (Russia)
• Portuguese (Portugal)
• Hindi (India)
• Urdu (Pakistan)
• Arabic (Saudi Arabia)
• French (France)
• English (UK) Premium
• English (Australia) Premium
• English (Canada) Premium
• English (India) Premium
• English (Singapore) Premium
• English (New Zealand) Premium
• English (South Africa) Premium
• Spanish (Spain) Premium
• Spanish (Mexico) Premium
• Spanish (United States) Premium
• Arabic (Egypt) Premium
• Arabic (United Arab Emirates) Premium
• Arabic (Kuwait) Premium
• Arabic (Bahrain) Premium
• Arabic (Qatar) Premium
• Arabic (Oman) Premium
• Arabic (Jordan) Premium
• Arabic (Lebanon) Premium
• Danish (Denmark) Premium
• German (Switzerland) Premium
• German (Austria) Premium
• French (Canada) Premium
• French (Switzerland) Premium
• French (Belgium) Premium
• Italian (Switzerland) Premium
• Dutch (Netherlands) Premium
• Dutch (Belgium) Premium
• Portuguese (Brazil) Premium
• Chinese (China) Premium
• Chinese (Taiwan) Premium
• Chinese (Hong Kong) Premium
• Chinese (Singapore) Premium
• Korean (South Korea) Premium
• Finnish (Finland) Premium
• Greek (Greece) Premium
• Czech (Czech Republic) Premium
• Swedish (Sweden) Premium
• Norwegian (Norway) Premium
• Turkish (Turkey) Premium
• Polish (Poland) Premium
• Romanian (Romania) Premium
• Hungarian (Hungary) Premium
• Thai (Thailand) Premium
• Hebrew (Israel) Premium
• Indonesian (Indonesia) Premium
• Vietnamese (Vietnam) Premium
• Malay (Malaysia) Premium
• Tagalog (Philippines) Premium
• Swahili (Kenya) Premium
• Swahili (Tanzania) Premium
• Zulu (South Africa) Premium
• Xhosa (South Africa) Premium
• Amharic (Ethiopia) Premium
• Tamil (India) Premium
• Tamil (Sri Lanka) Premium
• Bengali (Bangladesh) Premium
• Bengali (India) Premium
• Punjabi (Pakistan) Premium
• Punjabi (India) Premium
• Marathi (India) Premium
• Telugu (India) Premium
• Kannada (India) Premium
• Gujarati (India) Premium
• Oriya (India) Premium
• Malayalam (India) Premium
• Urdu (India) Premium
• Persian (Iran) Premium
• Azerbaijani (Azerbaijan) Premium
• Ukrainian (Ukraine) Premium
• Belarusian (Belarus) Premium
• Catalan (Spain) Premium
• Basque (Spain) Premium
• Galician (Spain) Premium
• Slovak (Slovakia) Premium
• Lithuanian (Lithuania) Premium
• Latvian (Latvia) Premium
• Estonian (Estonia) Premium
• Bulgarian (Bulgaria) Premium
• Albanian (Albania) Premium
• Croatian (Croatia) Premium
• Slovenian (Slovenia) Premium
• Bosnian (Bosnia and Herzegovina) Premium
• Serbian (Serbia) Premium
• Macedonian (North Macedonia) Premium
• Montenegrin (Montenegro) Premium
• Maltese (Malta) Premium
• Irish (Ireland) Premium
• Welsh (United Kingdom) Premium
• Scots Gaelic (United Kingdom) Premium
• Icelandic (Iceland) Premium
• Luxembourgish (Luxembourg) Premium
• Afrikaans (South Africa) Premium
• Hausa (Nigeria) Premium
• Yoruba (Nigeria) Premium
• Somali (Somalia) Premium
• Tigrinya (Eritrea) Premium
• Kinyarwanda (Rwanda) Premium
• Sesotho (Lesotho) Premium
• Shona (Zimbabwe) Premium
• Sinhala (Sri Lanka) Premium
• Dhivehi (Maldives) Premium
• Burmese (Myanmar) Premium
• Lao (Laos) Premium
• Khmer (Cambodia) Premium
• Mongolian (Mongolia) Premium
• Tibetan (China) Premium
• Uighur (China) Premium
• Pashto (Afghanistan) Premium
• Dari (Afghanistan) Premium
• Nepali (Nepal) Premium
• Dzongkha (Bhutan) Premium
• Sesotho (South Africa) Premium
• Setswana (Botswana) Premium
• Seselwa Creole (Seychelles) Premium
• Mauritian Creole (Mauritius) Premium
• Haitian Creole (Haiti) Premium
• Greenlandic (Greenland) Premium
• Faroese (Faroe Islands) Premium
• Samoan (Samoa) Premium
• Tongan (Tonga) Premium

Popular Writing Apps

This Free AI-Powered Acronym Generator creates unique abbreviations for any word or phrase, enhancing communication and clarity

This Free Active to Passive Voice Converter effortlessly transforms text, improving clarity and enhancing writing structure

This Free AI Answer Generator effortlessly generates answers to any question, providing valuable insights and solutions

LogicBall's Best AI Essay Writer creates high-quality, well-structured, and accurate essays in minutes, providing valuable content and saving time for users

Discover our Free AI Letter Generator for personalized formal or informal letters. Effortlessly craft messages and enhance communication

This Project Plan Generator creates detailed plans, providing you with structured frameworks for your projects efficiently

This Free AI Content & Copy Generator crafts high-quality & unique content and marketing copy, enhancing brand communication effortlessly

This Free AI Amazon Ad Headline Generator instantly creates compelling headlines, maximizing ad impact and driving sales

This AI Amazon Product Bullet Points Generator effortlessly creates compelling bullet points, enhancing product visibility and sales potential.

Case Study Generator

Unlock the power of our case study creator tool—Generate compelling case studies effortlessly with our creator and captivate your audience. With just a few clicks, our smart technology helps you understand data, find trends, and make insightful reports, making your experience better and improving your SEO strategy.

What is a Case Study

A case study is like a detailed story that looks closely at a particular situation, person, or event, especially in the business world. It's a way to understand how things work in real life and learn valuable lessons. For instance, if a business wanted to figure out how another one became successful, they might study that business as a case study.

Let's say there's a small company that started selling handmade products online and became successful. A case study about this business could explain the challenges they faced, the strategies they used to grow, and the results they achieved. By reading this case study, other businesses could learn useful tips and apply them to their situations to improve and succeed.

7 Tips For Writing Great Case Studies

• Pick a Familiar Topic: Choose a client or project that your audience can relate to. This makes it easier for them to see how your solutions might work for their situations.
• Clear Structure: Start with a concise introduction that sets the stage for the case study. Clearly outline the problem, solution, and results to make your case study easy to follow.
• Engaging Storytelling: Turn your case study into a compelling narrative. Use real-world examples, anecdotes, and quotes to make it relatable and interesting for your audience.
• Focus on the Problem: Clearly define the problem or challenge your case study addresses. This helps readers understand the context and sets the foundation for the solution.
• Highlight Solutions: Showcase the strategies or solutions implemented to overcome the problem. Provide details about the process, tools used, and any unique approaches that contributed to the success.
• Optimize for SEO: By incorporating your case study into a blog post using a blog post generator, you enhance its visibility and reach. This, in turn, improves the search engine rankings of your blog post, attracting more organic traffic.
• Quantify Results: Use data and metrics to quantify the impact of your solutions. Whether it's increased revenue, improved efficiency, or customer satisfaction, concrete results add credibility and demonstrate the value of your case study.

What is a Case Study Creator

A free case study generator is a tool or system designed to automatically create detailed case studies. It typically uses predefined templates and may incorporate artificial intelligence (AI) to generate comprehensive analyses of specific situations, events, or individuals.

This tool streamlines the process of crafting informative case studies by extracting key details, analyzing data, and presenting the information in a structured format.

Case study generators are valuable for businesses, students, or professionals seeking to efficiently produce well-organized and insightful case studies without the need for extensive manual effort.

Benefits of Using Case Study Generator

In today's competitive landscape, showcasing your product or service successes is vital. While case studies offer a compelling way to do this, starting from scratch can be time-consuming. That's where case study generators step in, providing a robust solution to streamline the process and unlock various advantages.

• Easy and Quick: A case study generator makes it simple to create detailed studies without spending a lot of time. It's a fast and efficient way to compile information.
• Accessible Online: As an online case study generator, you can use it from anywhere with an internet connection. No need for installations or downloads.
• Free of Cost: Many case study creators are free to use, eliminating the need for any financial investment. This makes it budget-friendly for businesses or individuals.
• AI-Powered Insights: Some generators use AI (artificial intelligence) to analyze data and provide valuable insights. This adds depth and accuracy to your case studies.
• Save Time and Effort: Generate a polished case study in minutes, automating tasks like data analysis and content creation. This frees up your time to focus on other aspects of your business.
• Enhance Quality and Consistency: Case study creators offer templates and AI-powered suggestions, ensuring your studies are well-structured and visually appealing. Consistent quality strengthens your brand image.
• Improve Brand Awareness and Credibility: Sharing case studies on your platforms increases brand awareness and builds trust. Positive impacts on others establish you as a credible provider.
• Boost Lead Generation and Sales: Compelling case studies build trust and showcase your value, attracting leads and converting them into customers, ultimately boosting your sales.
• Increase Customer Engagement and Loyalty: Case studies provide insights into your company, fostering deeper connections, increasing engagement, and promoting long-term loyalty.
• Improve Your Writing Skills: Free AI Case study generators act as learning tools, offering guidance on structure, content, and storytelling. Studying generated drafts refines your writing skills for crafting impactful case studies in the future.

How AI Case Study Generator Works

An online case study generator works by leveraging artificial intelligence algorithms to analyze and synthesize information, creating comprehensive case studies. Here's a simplified explanation of its functioning:

Data Input:

Algorithm analysis:, content generation:, language processing:, who needs a case study creator.

Anyone looking to create informative and detailed case studies can benefit from using an online case study generator. This tool is useful for

Businesses:

Professionals:, individuals:, marketing professionals:, researchers:, why opt for our case study creator.

Are you on the lookout for a top-notch case study generator that combines outstanding features with user-friendliness, all at no cost and without the need for registration? Your search ends here. Our AI-driven case study generator is the ideal solution for you. Here's why you should choose our tool:

Craft Case Study in 50+ Languages:

Incorporate keywords in case study:, user-friendly interface:, 100% free, no registration:, 20+ diverse tones for versatile styles:, how much does your case study creator cost, do i need any writing experience to use a case study generator, what types of case studies can i create with a case study creator, what are some common mistakes people make when creating case studies.

• Not focusing on the benefits to the reader.
• Not using data and results to support their claims.
• Not telling a compelling story.
• Not using visuals effectively.
• Not promoting their case study.

Can I customize the generated case study?

Is the generated content unique.

Caseen - Interactive Clinical Case Platform

AI Case Study Creator That Brings Stories to Life

Easily create impressive interactive case studies that increase lead engagement and conversion rates.

Used by professional marketing teams at:

Professional case study templates built for storytelling

Simply grab a template and let our AI case study generator✨ bring it to life for you:

How our AI case study generator works

Generate your case study with ai.

Simply type in what you need and let Storydoc do the magic for you!

Edit and bring it to perfection

Let our magic assistant help you through the process
with automatic slide copy and design.

Turbo-charge with integrations

Easily connect your CRM, calendar, and other tools
to move from static PDFs to actionable case studies.

Send. Track. Convert. Track customer engagement and conversion in real-time Generate your case study with AI

Send. track. convert..

Track customer engagement and conversion in real-time

Their case studies are getting attention

“Storydoc gives us the power and flexibility to design case studies and other pieces of content ourselves, even with our limited design experience ."

“With our most recent Storydocs, we're like, ‘Oh my goodness, It brought it to life like we do when we present it , but without the person even being there!’”

" Storydoc sets me apart from my competitors .  My goal is for my business to be memorable and Storydoc allows me to showcase the colors of my business in the best possible way."

A business case studies tool you can trust

Turn your case studies into an engagement tool.

Switch from static PDFs and webpages to interactive case studies created with modern marketing needs in mind.

Stop killing engagement

Readers strongly dislike PDF content . Replace your static case studies with interactive multimedia stories users love and remember.

Grant yourself content superpowers with AI

Easily design amazing interactive case studies by yourself faster than ever, guided by AI. No coding, no design skills needed.

Don’t lose your mobile readers

32% of case studies are opened on mobile  - your storydocs won’t fail to impress on mobile or any other device.

Convert users directly from your case studies

Enable readers to easily take the next step directly from your case studies with smart CTAs like a form, calendar, or live chat.

Wanna know if your case studies are working?

Get real-time analytics on everything . Reading time, scroll depth, conversions, shares, and more.

Make personalized case studies for ABM

Easily personalize prospecting case studies . Add the prospect's name and title with dynamic variables and instantantly apply their branding.

Your readers want a story , not a case study

Give'em what they want, give'em a Storydoc.

Everything that you should know about Storydoc

What is the Storydoc case study creator?

This AI case study generator lets you to intuitively design and write engaging interactive stories that captivate prospects. No coding or design skills needed.

The Storydoc case study designer offers a broad array of interactive slides for startups and new business concepts. These can be quickly and easily customized to align with your vision and requirements.

Storydoc frees you from outdated PPT slide methods, offering instead a scroll-based, web-friendly, mobile-optimized experience, complete with performance analytics.

Is the Storydoc AI case study generator safe?

Absolutely, the Storydoc AI case study creation app is secure and reliable. Your personal information is well-protected and encrypted.

We prioritize your data security, adhering to stringent security policies and best practices. Don't just take our word for it; companies like Meta, Pepsi, and Xerox trust us enough to use Storydoc daily.

For more information see  Our Story page ,  Terms and Conditions , and  Privacy Policy .

Why Storydoc is more than just another AI case study creator?

Storydoc is more than a tool for creating presentations. Instant AI case studies are useful, but they can become repetitive.

Sure, you can create your content faster, but does it truly stand out? Will it be effective? Probably not.

The issue often lies in the traditional PowerPoint design, whether AI-assisted or not. Storydoc takes a different approach.

We create case study experiences that truly engage decision-makers, featuring scrollitelling, multimedia, and in-document navigation.

Check out these examples .

What’s so great about AI-generated case studies?

An AI-generated case study can save you hours, even days, of effort for your startup. However, if you're using an AI PPT case study tool, you're saving time but potentially missing impact.

No one enjoys PowerPoints, even those created with AI. No AI PowerPoint case study tool can deliver a presentation that truly makes a difference. But Storydoc can. Our AI helps you create stories that generate interest and revenue.

Is Storydoc a free case study designer?

The Storydoc AI case study generator enables you to create content faster and more effectively than doing it solo.

Transform your presentations from ordinary to extraordinary in no time. Storydoc offers a 14-day free trial.

Try it out and see if it suits your needs. Based on hundreds of thousands of presentation sessions, we're confident that prospective clients will appreciate it.

Every interactive case study you create during your trial is yours to keep forever, at no cost!

For learning about our paid plans see our  Pricing .

Can I trust Storydoc with my data?

You can trust Storydoc to keep your personal information and business data safe.

The Storydoc app is safe and secure thanks to an encrypted connection . We process your data in accordance with very strict policies.

For more information, see Terms and Conditions , and Privacy Policy .

What's the best way to get started?

The easiest way to start is to visit our Case study templates page , pick a template you like, provide a few details, and see the magic happen - how Storydoc generates a presentation from scratch with your branding, content structure, visuals, and all.

Inside the presentation maker app, you can switch between templates, adjust your design with drag and drop interface, find ready-made slides for any use case, and generate text and images with the help of our AI assistant.

How do I send or share Storydoc case studies?

Storydocs function like web pages; each case study you create has a unique link for easy sending and tracking.

Once your Storydoc is complete, just hit publish. Published presentations are instantly viewable in any browser.

To share your presentation, simply click the Share button and copy the link. Viewers will experience an interactive webpage, far more engaging than a static PowerPoint or PDF.

Can I print Storydoc case studies?

Yes, but currently, this service is only available to our Pro and Enterprise customers. However, this feature will soon be accessible to all Storydoc users directly from the editor.

Keep in mind, a printed Storydoc loses its interactive elements, which are key to its high engagement and charm.

What integrations does Storydoc offer?

All the essential ones! Storydocs provide full content integrations: Calendly, Loom, YouTube, Typeform, and more, all of which can be added to your Storydoc presentation. But we offer much more than the basics.

With Storydoc, you can embed lead-capturing forms, your own live chat, advanced dashboards, in-page payments, and e-signatures.

Learn more on our Integrations page .

Are Storydocs mobile-friendly?

Yes! Storydoc is optimized for flawless mobile performance . No matter the divide or OS your case studies is opened on, the design will be perfect.

Check out similar Storydoc tools

Create your best case study to date

Stop losing opportunities to ineffective case studies. Your new winning case study is one click away!

Medical Case Study Template

Utilize our Medical Case Study Template for systematic documentation and analysis of patient cases, enhancing learning and clinical practice.

By Emma Hainsworth on Apr 08, 2024.

Fact Checked by Ericka Pingol.

What is a Medical Case Study Template?

A Medical Case Study Template is a systematic tool used primarily by healthcare professionals and students to document, analyze, and present individual patient cases. It serves as a structured guide, ensuring that all critical aspects of the case are captured and discussed in a standardized format. This includes patient information, clinical history, diagnostic assessments, treatments, and outcomes.

The template typically starts with a title and a brief abstract summarizing the case. It progresses through detailed sections covering patient demographics, symptoms, examination findings, and the results of any diagnostic tests. Treatment plans and their outcomes are also meticulously documented.

One of the key purposes of this template is educational. It facilitates learning by providing a comprehensive overview of real-life clinical scenarios, encouraging critical thinking and analysis. It's also used in medical research and literature to share clinical experiences and insights, contributing to the broader medical community's understanding of various conditions and treatments.

Using a Medical Case Study Template ensures a thorough and uniform approach to case documentation, which is essential for effective learning, discussion, and advancement in medical practice. It emphasizes the importance of detail and accuracy in clinical reporting, enhancing both educational value and patient care.

Printable Medical Case Study Template

Download this Medical Case Study Template used primarily by healthcare professionals and students to document, analyze, and present individual patient cases.

How does it work?

Here's a step-by-step guide to accessing and using our Medical case study template:

Step 1: Access and download the Medical Case Study Template

Click the link on this page to download and access the Medical Case Study Template. The template is available in a user-friendly PDF format, allowing for easy digital viewing and interaction.

You can print the template if a physical copy is preferred for note-taking or group discussions. This is especially useful in educational settings or during team meetings.

Step 2: Understand the structure of the template

Review the template to understand its various sections, which include patient information, clinical history, diagnosis, treatment, and outcome.

Note the specific details required in each section, such as demographic data, symptoms, test results, and follow-up care. Understanding these components is crucial for consistency and comprehensiveness in case documentation.

Step 3: Utilize the template for case documentation

Use the template to systematically record all relevant details of a patient case. This might be for educational purposes, research, or clinical documentation.

Regularly update the template with new information or follow-up details. This is important for tracking the case's progression and observing treatments' effectiveness.

Keep in mind that each patient case is unique. The template should be adaptable to accommodate the specificities of different medical scenarios.

Step 4: Review and share the documented case

Use the documented case for peer discussions, educational sessions, or team reviews. The structured format of the template facilitates easy sharing and collaborative analysis.

Encourage feedback on the documented case to enhance learning and improve clinical practices. The template can be a tool for reflective practice and continuous professional development.

By following these steps, healthcare professionals, educators, and students can effectively use the Medical Case Study Template for a range of purposes. This resource is invaluable for documenting individual cases and as a tool for education, research, and collaborative learning in the medical field.

Medical Case Study Example (sample)

We have provided a comprehensive example as a downloadable PDF to help users effectively utilize the Medical Case Study Template. This sample case study is a practical guide, demonstrating how to document and analyze a clinical case in a structured and detailed manner. It is especially useful for medical professionals, educators, and students in the healthcare field.

The Medical Case Study PDF Example, readily available for download and review, showcases the practical application of the case study template in a real-world clinical setting. The example features a case study highlighting how to systematically record patient information, clinical history, diagnostic assessments, treatment plans, and outcomes.

You can access this valuable resource by either previewing the sample provided below or clicking the "Download Example PDF" button. 

Download this Medical Case Study Template example:

When would you use this Template?

The Medical Case Study Template is an invaluable resource primarily used by healthcare professionals, medical students, and educators in various clinical and educational settings. Its application is particularly relevant and beneficial in several key scenarios.

Clinical education and training

Medical educators often use this template to teach students about real-life medical cases. It provides a structured approach to case analysis, encouraging students to examine all aspects of patient care, from diagnosis to treatment and follow-up. This methodical approach is crucial in developing critical thinking and diagnostic skills.

Peer learning and discussion

The template is used among healthcare professionals as a basis for case discussions, often in grand rounds or peer review meetings. It allows practitioners to present complex cases to colleagues, fostering collaborative analysis and discussion. This shared learning experience can lead to improved patient care strategies and a deeper understanding of rare or challenging cases.

Research and case reporting

When documenting unique or particularly instructive cases for medical research or publication, the Medical Case Study Template provides a comprehensive format. It ensures that all relevant details are systematically presented, making the case valuable for the wider medical community. This is especially important when contributing to medical journals or databases.

Quality improvement initiatives

In quality assurance and improvement activities within healthcare settings, this template can be used to analyze cases that highlight systemic issues or successes. It aids in identifying areas for improvement in patient care processes and protocols.

Personal professional development

For individual healthcare practitioners, the template serves as a tool for reflecting on challenging cases, allowing for self-assessment and continuous professional development.

Benefits of using this template ? 

Using a Medical Case Study Template offers several significant benefits:

• Standardized documentation: The template provides a uniform structure for documenting patient cases. This standardization is crucial for ensuring that all relevant details are consistently recorded and easy to find, which is particularly beneficial when multiple healthcare professionals review cases.
• Enhanced educational value: For medical students and trainees, the template is an educational tool that promotes a thorough understanding of clinical cases. It encourages a holistic view of patient care, encompassing diagnosis, treatment, and follow-up, and helps in developing critical thinking and analytical skills.
• Improved communication: When discussing patient cases with peers or in academic settings, the template aids in clear and concise communication. It ensures that all necessary information is presented in an organized manner, facilitating better understanding and discussion among medical professionals.
• Quality of care and patient safety: By systematically documenting and reviewing patient cases, healthcare providers can identify trends, successes, and areas for improvement in treatment protocols. This can lead to enhanced patient safety and overall quality of care.
• Facilitates research and publication: The template is invaluable for practitioners looking to publish case reports in medical journals. It ensures that the case is documented in a comprehensive and systematic manner, meeting the standards required for academic and research publications.
• Professional development: Regular use of the template encourages practitioners to reflect on their clinical practice, helping them to identify areas for professional growth and development.
• Consistency in case analysis: The template provides a consistent framework for analyzing and discussing patient cases. This consistency is essential for comparative analysis and for understanding the nuances of different cases.
• Time efficiency: With a set structure in place, healthcare professionals can save time in documenting and reviewing cases, as they do not have to create a new format each time.

Commonly asked questions

A Medical Case Study Template is a structured document used to record and analyze patient cases in a systematic and detailed manner. It helps in documenting patient history, diagnosis, treatment, and follow-up, ensuring all crucial aspects of the case are covered.

Medical professionals, including doctors, nurses, and therapists, as well as medical students and educators, can benefit from using this template. It's also useful for researchers and clinicians involved in case studies for publication or presentation.

The template ensures comprehensive documentation of patient cases, which aids in better understanding and analyzing the patient's condition and treatment outcomes. This thorough approach can lead to more informed and effective patient care strategies.

Related Templates

Popular templates.

Action Planning Worksheet

Maximize project success with our Action Planning Worksheet. Track and measure progress effectively. Download free templates for complete project management.

Pharmacy Technician Worksheets

Unlock pharmacy tech skills with our free worksheets for exam prep, skill refreshment, and practical knowledge. Download now.

Type 2 Diabetes Treatment Guidelines

Get comprehensive guidelines and examples for treating Type 2 Diabetes in Carepatron's free PDF download.

Pain Management Treatment

Discover effective pain management treatments and examples through Carepatron's free PDF download. Learn about various strategies to alleviate pain and improve your quality of life.

Facts vs Feelings Worksheet

Differentiate between facts and feelings with our Facts vs Feelings Worksheet. Ideal for therapy sessions, promoting emotional regulation and logical thinking.

Gaslighting Worksheet

Use our Gaslighting Worksheet to empower your clients. Help patients recognize and overcome gaslighting, facilitate healing, and improve mental wellness.

Gratitude Jar Worksheet

Enhance well-being in your practice with our Gratitude Jar Worksheet. Empower your clients to cultivate positivity and gratitude in their lives.

Burnout Recovery Plan

Get your free PDF of a burnout recovery plan from Carepatron to help you overcome burnout and regain work-life balance. Explore practical recovery strategies.

Therapy Invoice Template

Our Therapy Invoice Template streamlines billing processes, enhances professionalism, and effortlessly keeps you organized. Download now!

Muscle Test

Discover everything you need to know about muscle testing, examples, and Carepatron's free PDF download to help you better understand this technique.

Five-Facet Mindfulness Questionnaire

Measure mindfulness with an evidence-based tool to gain clients' mindfulness profiles and improve clinical outcomes through tailored interventions.

Pisiform Fracture Test

Learn how to conduct the Pisiform Fracture Test. Get a free PDF template to record results and streamline your documentation.

90-90 Hamstring Test

Learn how to perform the 90-90 Hamstring Test. Access a free PDF template and example here.

Overhead Squat Assessment

Get access to a free Overhead Squat Assessment template. Learn how to perform this test and interpret the results.

Osteoarthritis Diagnosis Criteria

Understand the signs, causes, and effective criteria for diagnosis. Download our free PDF example for a detailed understanding.

Stages of Relapse Worksheet

Download our free Stages of Relapse Worksheet to effectively track and manage signs of emotional, mental, and physical relapse.

Cervical Flexion Rotation Test

Discover the Cervical Flexion Rotation Test for assessing upper cervical spine dysfunction, ideal for diagnosing cervicogenic headaches and neck issues.

Bipolar Treatment Guidelines

Explore expert Bipolar Treatment Guidelines, including diagnosis, symptom management, and personalized care strategies for improved health.

Levels of Hoarding Test

Access a resource that will aid you in evaluating a patient's hoarding disorder. Download our Levels of Hoarding Test today!

Inner Child Worksheets PDF

Try the transformative power of Inner Child Worksheets PDF. Heal past wounds, foster self-discovery, and embrace authenticity with our guide and template.

Mental Health Diagnosis Cheat Sheet

Discover our Mental Health Diagnosis Cheat Sheet for accurate ICD-10 coding, enhancing care and billing in mental health practices.

Antisocial Personality Disorder Treatment Plan

Discover an effective treatment plan for Antisocial Personality Disorder to help individuals manage symptoms and improve quality of life.

Vineland Adaptive Behavior Scale

Learn more about the comprehensive Vineland Adaptive Behavior Scale, which assesses adaptive behaviors in individuals and provides targeted support.

Norton Scale

Learn about the Norton Scale, a tool used in healthcare to assess the risk of pressure ulcers. Understand its significance and application.

Adjustment Disorder PDF

Navigate the complexities of adjustment disorder with our comprehensive PDF guide. Explore symptoms, diagnosis criteria, and treatment options here.

Medical Diagnosis Form

This form helps healthcare professionals gather patient information for accurate diagnosis. Download a free medical diagnosis form template.

Authenticity Test

Discover how authentic you are with our Authenticity Test. Uncover your true self, live more authentically, and improve your life satisfaction. Try it now!

IFS Treatment Plan

Download Carepatron's free PDF example of an Internal Family Systems (IFS) treatment plan. Learn how to create a comprehensive treatment plan using IFS therapy techniques.

Beers Criteria Template

Explore the Beers Criteria Template, guiding principles for safer medication use in older adults—essential knowledge for healthcare professionals.

Psychotic Disorders List

Download our free Psychotic Disorders List as a helpful reference tool based on the diagnostic criteria outlined in the DSM-5.

Social Communication Disorder Checklist PDF

Access a guided checklist to aid in diagnosing social communication disorder for earlier detection and intervention.

Non-Medical Home Care Assessment Form PDF

Download, print, and fill out our Non-Medical Home Care Assessment Form PDF for thorough evaluation and personalized care plans. Streamline your caregiving process today!

Miracle Question Worksheet

Use a helpful tool when practicing solution-focused therapy to engage and build motivation among clients. Download your free PDF here.

Premenstrual Dysphoric Disorder DSM-5

Explore the criteria of Premenstrual Dysphoric Disorder (PMDD) as outlined in DSM-5, shedding light on panic disorder and its impact on mental health.

AC Joint Test

Explore AC Joint Test procedures, results interpretation, and next steps for managing injuries like AC joint separation and arthritis.

Discover the ins and outs of the PVD Test with our comprehensive guide and template. Learn about its purpose, procedure, risks, and more.

Immature Personality Disorder Test

Assess and understand immature personality disorder with Carepatron's free PDF test and examples. Get insights into this personality disorder and learn more about it.

Stinking Thinking Worksheet

Download our Stinking Thinking Worksheet to help clients challenge negative thoughts and cognitive distortions.

Snapping Hip Test

Gain access to an essential resource, the Snapping Hip Test, to assist you in diagnosing a snapping hip during physical assessments. Download today!

R Worksheets for Speech Therapy

Enhance your speech therapy sessions with R Worksheets, designed to improve articulation skills. Download the free PDF sample today!

Hip Quadrant Test

Learn how detailed records promote quality of care for patients with hip pathologies. Download our Hip Quadrant Test to document the process.

Shame Resilience Theory Template

Download a free resource that clients can use for a more goal-directed approach to building shame resilience.

Disruptive Mood Dysregulation Disorder Treatment Plan

Unlock efficient anxiety care with Carepatron's software, featuring patient management tools, secure communication, and streamlined billing.

Multiple Sclerosis Test

Discover the symptoms, causes, diagnosis, and treatment options for Multiple Sclerosis and understand comprehensive care approaches.

MCL Injury Test

Discover key insights on MCL injuries, from symptoms and diagnosis to recovery. Get expert advice for effective treatment and healing.

Pediatric Depression Screening Tool

Explore the benefits of Pediatric Depression Screening Tools for early detection and effective treatment in children and adolescents.

Dermatomyositis Diagnosis Criteria

Learn about the diagnostic criteria for dermatomyositis and see an example with Carepatron's free PDF download. Get the information you need to understand this condition.

Pathological Liar Test

Take a pathological liar test and explore examples to understand this behavior better. Download Carepatron's free PDF for further information and insights.

Brief Dissociative Experiences Scale

Explore the Brief Dissociative Experiences Scale, a tool for assessing dissociation in just a few items. Understand its significance and application.

Hip Mobility Test

Learn how to assess your hip mobility with a simple test. Download Carepatron's free PDF guide with examples to improve hip flexibility and function.

Massage Chart

Explore our comprehensive Massage Chart for holistic healthcare solutions and techniques. Perfect for practitioners and enthusiasts alike!

Occupational Therapy Acute Care Cheat Sheet

Streamline patient recovery with key strategies for enhancing independence in acute settings. Download our free Occupational Therapy Acute Care Cheat Sheet.

Neck Pain Chart

Explore our comprehensive Neck Pain Chart for insights into causes, symptoms, and treatments. Find relief and understanding today.

School Readiness Assessment

Discover the benefits of our School Readiness Assessment template with a free PDF download to ensure children are prepared for kindergarten through grade three.

Sedation Scale Nursing (Ramsay Sedation Scale)

Discover key sedation scales used in nursing, including RASS and Ramsay Scale, for accurate patient sedation assessment in critical care settings.

Blood Tests for Lupus

Download a free Blood Tests for Lupus template. Learn the various types of tests used to diagnose this disease.

Neurological Exam Template

Discover the importance of neurological assessments for diagnosing and monitoring conditions with our comprehensive guide and template. templates

Homeopathic Dosage Chart

Discover the principles of homeopathy, dosage guidelines, and effectiveness, with references and a downloadable chart for practical use.

Gout Diagnosis Criteria

Uncover the essential Gout Diagnosis Criteria here, including symptoms, diagnostic tests, and the American College of Rheumatology guidelines.

Peroneal Tendon Tear Test

Learn about the Peroneal Tendon Tear Test and use our template to record results!

Ear Seeds Placement Chart

Learn what ear seeds are and download our Ear Seeds Placement Chart template!

Global Rating of Change Scale

Explore the Global Rating of Change Scale, a key tool to assess the clinical significance of patient-reported outcomes. Get your free PDF template now.

Cognitive Conceptualization Diagram

Create a visual map to help explore the connection between a client's experience and response and their underlying thoughts and emotions.

Occupational Therapy Goals

Find out how you can set effective occupational therapy goals with the SMART goal framework and our free template. Download your PDF here.

Physical Therapy Initial Evaluation

Unlock the benefits of Physical Therapy Initial Evaluation: a key step in personalized treatment planning for improved mobility and pain relief.

8-Week Group Counseling Plan

Discover a flexible template for creating effective 8-Week Group Counseling Plans, tailored to meet diverse needs and foster growth.

Osteoarthritis Treatment Guidelines

Learn about the management of osteoarthritis, treatments, causes, and care strategies. Access tips for managing symptoms and improving quality of life in our guide.

Prone Knee Bend Test

Learn about the Prone Knee Bend Test and how it assesses radicular pain.

PTSD Treatment Guidelines

Download our PTSD Treatment Guidelines template to access evidence-based treatment plans, diagnostic tools, and personalized care strategies.

DBT House PDF

Learn about DBT House and use it to help patients work through their issues.

Assess alcohol use with AUDIT-C: quick, accurate alcohol screening tests for hazardous drinking. Learn your risk level in minutes. Download our free template now!

Cincinnati Stroke Scale Scoring

Learn to quickly identify stroke symptoms with the Cincinnati Stroke Scale Scoring guide, which is essential for emergency responders and healthcare professionals.

Managing Expectations Worksheet

Empower clients to guide their mental health with our Managing Expectations Worksheet. Foster clarity and growth in realistic expectations.

Lupus Diagnosis Criteria

Download a helpful checklist tool to help diagnose lupus among patients for early identification and intervention. Access your free resource here!

Quadriceps Strain Test

Get access to a free Quadriceps Strain Test template. Learn how to interpret results and streamline your documentation with a free PDF.

Dysarthria Treatment Exercises

Discover effective dysarthria exercises to improve speech clarity. Download our free guide for tailored speech therapy techniques.

Depression Treatment Guidelines

Discover Carepatron - the ultimate mental health software for enhanced care, efficient scheduling, seamless billing, and secure communication.

Antisocial Personality Disorder Test

Use a helpful evidence-based Antisocial Personality Disorder Test to identify ASPD symptoms among clients and improve health outcomes.

Persistent Depressive Disorder Test

Explore an evidence-based screening tool to help diagnose persistent depressive disorder among clients.

Thinking Traps Worksheet

Unlock a healthier mindset with our Thinking Traps Worksheet, designed to identify and correct cognitive distortions. Download your free example today.

Coping Cards

Coping Cards can aid clients in managing distressing emotions. Explore examples, download a free sample, and learn how to integrate them into therapy effectively.

EFT Cycle Worksheet

Download our free EFT Cycle Worksheet example and discover the benefits of integrating it into your practice.

Torn Meniscus Self Test

Discover how to identify a torn meniscus with our self-test guide. Learn about meniscus function and symptoms, and download our free self-test template today.

Prediabetes Treatment Guidelines

Download a free Prediabetes Treatment Guidelines and example to learn more about managing prediabetes effectively.

Nurse Practitioner Performance Evaluation

Enhance clinical performance, communication, and patient satisfaction in your healthcare system. Download our Nurse Practitioner Performance Evaluation today!

Psychophysiological Assessment

Explore Psychophysiological Assessments with our free template. Understand the link between mental processes and physical responses. Download now.

Occupational Therapy Pediatric Evaluation

Learn about the process of pediatric occupational therapy evaluation. Download Carepatron's free PDF example to assist in understanding and conducting assessments for children.

Dementia Treatment Guidelines

Explore comprehensive dementia treatment guidelines and use Carepatron's free PDF download of an example plan. Learn about effective strategies and interventions for dementia care.

Coping with Auditory Hallucinations Worksheet

Use our Coping with Auditory Hallucinations Worksheet to help your patients manage and differentiate real sensations from hallucinations effectively.

Tight Hip Flexors Test

Assess the tightness of your patient's hip flexor muscles with a tight hip flexors test. Click here for a guide and free template!

Gender Dysphoria DSM 5 Criteria

Explore the DSM-5 criteria for Gender Dysphoria with our guide and template, designed to assist with understanding and accurate diagnosis.

Bronchitis Treatment Guidelines

Explore bronchitis treatment guidelines for both acute and chronic forms, focusing on diagnosis, management, and preventive measures.

Patient Safety Checklist

Enhance safety in healthcare processes with the Patient Safety Checklist, ensuring comprehensive adherence to essential safety protocols.

Fatigue Assessment

Improve workplace safety and employee well-being with this valuable resource. Download Carepatron's free PDF of a fatigue assessment tool here to assess and manage fatigue in various settings.

AC Shear Test

Get access to a free AC Shear Test. Learn how to perform this assessment and record findings using our PDF template.

Hip Flexor Strain Test

Access our Hip Flexor Strain Test template, designed for healthcare professionals to diagnose and manage hip flexor strains, complete with a detailed guide.

Integrated Treatment Plan

Explore the benefits of Integrated Treatment for dual diagnosis, combining care for mental health and substance abuse for holistic recovery.

Nursing Assessment of Eye

Explore comprehensive guidelines for nursing eye assessments, including techniques, common disorders, and the benefits of using Carepatron.

Nursing Care Plan for Impaired Memory

Discover our Nursing Care Plan for Impaired Memory to streamline your clinical documentation. Download a free PDF template here.

Assessing mania symptoms is crucial for mental health awareness. Download Carepatron's free PDF example of a mania test to understand the evaluation process better.

Height Weight Charts

Learn about height-weight charts and their use in healthcare practice. Find out your ideal weight and stay healthy!

Glasgow Coma Scale

The Glasgow Coma Scale (GCS) dates back to the 1970s and has become one of the most widely accepted measurements of impairment following brain injury. What better way to start using the GCS in your practice than with Carepatron’s free PDF GCS template download.

A1C Goals By Age Chart

Use our free A1C Goals by Age Chart to learn the recommended A1C goal for people of different ages. Download our free PDF and example here.

Weight Height Chart For Women

Uncover insights on the ideal BMI for women with our comprehensive height-weight chart, offering accurate benchmarks for healthy living and fitness.

Rhomboid Manual Muscle Test

Learn how to detect Rhomboid muscle weakness by conducting the Rhomboid Manual Muscle Test!

NANDA Nursing Care Plan

Enhance patient care with our NANDA Nursing Care Plan Template. Streamline assessments, interventions, and evaluations for efficient healthcare delivery.

Fever Temperature Chart

Track your health with precision using our Fever Temperature Chart. Monitor temperature symptoms and get medical help for optimal care.

Manual Muscle Testing

Improve patient assessment skills with our Manual Muscle Testing template. Download for free and easily record and interpret muscle strength results.

Pain Level Chart

Discover the utility of Pain Level Charts for effective pain assessment and communication. Download our free PDF template and learn how to use it effectively.

Admission Nursing Note

Discover what is included in an Admission Nursing Note and download a free PDF example to get started.

ALT Blood Test

Discover the ALT Blood Test, including what it is, how it works, when to use it, and interpret results. Free ALT Blood Test example available for download.

Palliative Performance Scale

Learn about the Palliative Performance Scale and its application in hospice care. Download a free PDF template and follow our guide to using the PPS effectively.

Join 10,000+ teams using Carepatron to be more productive

Case Study Generator

It’s free and easy to use. A simple upload of your content and your Case Studies are autogenerated.

Create Case Studies by repurposing audio or video content

The AI works fully automated

No prompt engineering required

Experience the power of AI without needing prompts

Train your AI to exactly sound like you

You can train your AI on a piece of content of yours

Autogenerate Case Studies

Create Case Studies based on the best templates from top-marketers. You will get your insights in the best format possible.

Extracted from your existing content

Work with your team, edit and download Case Studies

Case Studies that sound like you

You can train your Case Study Generator to sound like you, so you Case Studies are always in your own tone and style—the opposite of generating generic-sounding content out of thin air.

AI-based content repurposing for your Case Studies

Creating Case Studies is part of every good content marketing strategy. And now it has become even more accessible with Case Study Generator.

The best Case Study Generator.

Create Case Studies based on your insights, and that sounds like you inside Unifire, thanks to all the Case Study Generator’s unique AI-powered features.

Your content is generated based on the best creator templates and hook frameworks.

Collaboration

Unifire is built for collaboration. Live editing, unlimited team members and workspaces included.

Train your AI

AI’s that can sound like you and learn your own writing style. From vocabulary to sentence structure.

What are Case Studies?

Case studies are an incredibly effective tool for demonstrating your expertise and building trust with your audience. They provide real-life examples of how you’ve handled specific situations, solved problems, or achieved results for your clients. By showcasing these success stories on your website or blog, you can help potential customers understand the value of your services. Remember, a well-crafted case study can be a powerful SEO tool, driving organic traffic to your site and improving your search engine rankings.

How to use the Case Study Generator

It couldn’t be easier: Upload a piece of content, audio or video. Let the tool transcribe it and produce your Case Studies.

You can upload audio & video files, directly or via a link. After 2-4 mins you will receive your transcript. Autogenerated.

Auto transcription

We have the best transcription running in the background. 97% accuracy on even the most technical topics speaks for itself.

Edit your transcription

No AI is 100% perfect. So, we let you edit your transcript before hitting the generate content button.

Autogeneration

Case Study Generator automatically generates your Case Studies, based on the best templates we could find. So your content is in the best shape when you get it.

Edit & Collaborate

Case Study Generator is entirely collaborative and comes with unlimited team seats, workspaces and a full collaborative suite.

Once you’re ready to distribute, simply copy & paste your content into your favourite tools.

Questions about the Case Study Generator

It’s free for your first upload. You get 900 credits every month for free to use.

This is dependent on the length of your upload. If you upload 5 hours it will create much more content than if you upload only 5 mins.

Yes, you can train the AI to adopt your own tone & style. This includes sentence structures, vocabulary and more.

Some of the best Case Study Generator include Jasper, Reword, Anyword, and others.

Free Twitter Thread Maker for creating perfect Twitter Threads

Viral generator for creating perfect viral content, linkedin post caption generator for creating perfect linkedin captions, ebook generator for creating perfect e-books, ai podcast clip generator for creating perfect podcast clips, ai-based content generator for creating perfect content, twitter post generator for creating perfect twitter posts, article headline generator for creating perfect article headlines, ai linkedin post generator for creating perfect linkedin posts, youtube time link generator for creating perfect youtube timelines, your case studies deserve to be perfect, and so does the rest of your content.

Repurpose and scale content whether you are a seasoned marketer or a total novice.

Bulk Content Generator

Brand Voice

AI Text Editor

Case Study Generator

Craft a detailed case study utilizing the given details to captivate and optimize for SEO.

Try Case Study for free →

Learn how to provide the key details and context for the case study to generate a comprehensive and engaging document

1 variation

A Case Study Generator is a powerful tool designed to automatically create detailed case studies with the help of AI writing assistance. It plays a crucial role in showcasing business successes, attracting new clients, and establishing credibility within the industry.

With the rise of AI technology, creating case studies has been completely transformed. Now, it's possible to generate customized, top-notch case studies quickly and easily with the help of AI.

Junia AI 's Case Study Generator offers an innovative solution that elevates your storytelling efforts and sets you apart from the competition.

How Does Junia AI's Case Study Generator Work?

Junia AI's Case Study Generator is different because of how it creates case studies automatically. It uses smart AI algorithms to help with writing, making sure that the case studies created are of high quality and tailored to specific needs. The platform also has templates that can be customized, which helps in making the case study look good and organized.

• Advanced AI writing assistance algorithms
• Customizable templates

This combination of features makes it easy to create visually appealing and cohesive case study presentations.

Streamlining the Creation Process

The main goal of Junia AI's Case Study Generator is to make the process of creating case studies faster and more efficient. With this tool, users don't have to start from scratch or spend hours writing each section. Instead, they can input their information and let the AI do the rest.

• Tailored to user's needs and branding

Generating Compelling Narratives

One of the key strengths of Junia AI's Case Study Generator is its ability to generate compelling narratives based on data and content provided. The advanced algorithms analyze the information given and turn it into a story that engages readers.

• Analyzes data and content
• Creates compelling narratives

Ensuring Consistency and Coherence

Another advantage of using Junia AI's Case Study Generator is that it maintains consistency and coherence throughout the case study. This means that all sections flow well together and there are no abrupt changes in tone or style.

• Maintains consistency
• Ensures coherence

By combining these three elements - streamlined creation process, compelling narratives, and consistency/coherence - Junia AI's Case Study Generator helps businesses create effective case studies that showcase their success stories in a clear and persuasive manner.

Diverse Distribution Opportunities with Junia AI's Case Study Generator

Versatile distribution formats.

Junia AI's Case Study Generator offers a wide range of options for sharing your case studies, including:

• PDFs : Perfect for presentations or downloadable resources.
• Website integration : Seamlessly embed your case studies on your website for easy access by publishing your case study to your CMS systems, such as WordPress or Shopify .

Benefits of Using Blog Posts

One effective way to showcase the case studies you create with Junia AI is through blog posts . Here's why:

• Maximum reach : Blog posts have the potential to reach a large audience, helping you get your message out to more people.
• SEO advantages : By optimizing your blog posts with relevant keywords and links, you can improve your search engine rankings and attract organic traffic.

Easy Link Sharing for Collaboration

Junia AI understands the importance of collaboration and client presentations. That's why they've made it simple to share your case studies with others:

• Convenient link sharing : Generate unique links for each case study, making it easy to send them to clients or colleagues.
• Real-time updates : Any changes you make to the case study will automatically be reflected in the shared link, ensuring everyone is always viewing the latest version.

By utilizing these diverse distribution options, businesses can effectively showcase their case studies, reach a wider audience, and drive meaningful engagement.

Using a Case Study Generator can greatly enhance your storytelling efforts and establish credibility in your industry. The automation and AI technology offered by platforms like Junia AI's Case Study Generator can streamline the process of creating high-quality and tailored case studies, saving you time and effort.

By using a Case Study Generator like Junia AI, you can:

• Unlock your creativity and deliver compelling narratives that captivate your audience.
• Optimize case study performance and drive user interaction and conversion with customizable templates, real-time engagement tracking, and smart CTAs.
• Showcase your expertise and build trust with your target audience through generating personalized narratives with dynamic variables and branding application supported by Junia AI.
• Ensure maximum reach and SEO benefits by distributing case studies in various formats such as PDFs, website integration and blog posts.
• Impress potential clients, drive customer engagement, and ultimately achieve business success.

So why not leverage this innovative solution to elevate your storytelling efforts and establish yourself as an industry leader?

Example outputs

Generate engaging case studies effortlessly with our Case Study Generator

How XYZ Company Increased Their Organic Traffic by 50%

XYZ Company is a leading provider of software solutions for small businesses. They had been struggling to increase their organic traffic despite having a well-designed website and regularly publishing blog posts.

After conducting an SEO audit, we identified several areas where XYZ Company could improve their search engine rankings. We recommended the following strategies:

• Conducting keyword research to identify high-value keywords that were relevant to their target audience
• Optimizing on-page elements such as title tags, meta descriptions, and header tags
• Improving site speed and mobile responsiveness
• Building high-quality backlinks from authoritative websites in their industry

Within six months of implementing our recommendations, XYZ Company saw a 50% increase in organic traffic. Their website now ranks on the first page of Google for several high-value keywords, driving more leads and sales to their business.

How ABC Agency Helped a Local Restaurant Increase Their Online Visibility

A local restaurant was struggling to attract new customers through their online presence. Despite having a website and social media profiles, they weren't getting much engagement or visibility.

We conducted a comprehensive digital marketing audit and found several opportunities to improve the restaurant's online visibility. Our strategy included the following tactics:

• Creating a content marketing plan to publish regular blog posts and social media updates
• Optimizing the restaurant's website for local search with targeted keywords and location-based landing pages
• Running paid advertising campaigns on Facebook and Instagram to reach new audiences
• Implementing email marketing campaigns to keep existing customers engaged and encourage repeat visits

Within three months of implementing our strategy, the restaurant saw a significant increase in online visibility and engagement. Their website traffic increased by 75%, and they saw a 50% increase in social media engagement. The restaurant also reported an increase in foot traffic, with many customers mentioning that they found the restaurant through their online presence.

How DEF Company Increased Their E-commerce Sales by 200%

DEF Company is an e-commerce retailer selling fashion accessories. They had been struggling to increase their sales despite having a wide range of products and competitive pricing.

We conducted a thorough analysis of DEF Company's website and identified several areas where they could improve their user experience and conversion rate. Our strategy included the following tactics:

• Conducting customer research to identify pain points and opportunities for improvement
• Redesigning the website to improve navigation and make it more visually appealing
• Implementing a mobile-responsive design to cater to the growing number of mobile shoppers
• Improving product descriptions and images to provide more information and enhance the shopping experience
• Running targeted advertising campaigns on Google AdWords and Facebook Ads

Within six months of implementing our recommendations, DEF Company saw a 200% increase in e-commerce sales. Their website now ranks on the first page of Google for several high-value keywords, driving more leads and sales to their business.

What other amazing things can this template help you create?

✔ Meta Title

✔ Meta Description

✔ Extract keywords

✔ Feature Image

✔ Soon Internal Linking

Who needs Case Study Generator?

Marketing teams

Content creators

Business owners

Entrepreneurs

Frequently asked questions

• How does Junia AI's Case Study Generator work? Junia AI's Case Study Generator is different because of how it streamlines the creation process, generates compelling narratives, ensures consistency and coherence, and offers diverse distribution opportunities with versatile formats. It uses advanced algorithms to automate the case study creation process, saving time and effort for users.
• What is the main goal of Junia AI's Case Study Generator? The main goal of Junia AI's Case Study Generator is to make the creation process more efficient and effective. By automating the generation of compelling narratives and ensuring consistency and coherence, it aims to provide users with a powerful tool for showcasing their success stories.
• What are the key strengths of Junia AI's Case Study Generator? One of the key strengths of Junia AI's Case Study Generator is its ability to generate compelling narratives that captivate audiences. By leveraging advanced algorithms, it can create engaging stories that effectively showcase the success of a product or service.
• What are the advantages of using Junia AI's Case Study Generator? Another advantage of using Junia AI's Case Study Generator is its ability to ensure consistency and coherence across all generated content. This helps maintain a unified brand voice and message, enhancing the overall impact of the case studies.
• What distribution opportunities does Junia AI's Case Study Generator offer? Junia AI's Case Study Generator offers diverse distribution opportunities with versatile formats. Users can easily share their case studies through various channels such as blogs, social media, websites, and more, reaching a wider audience and maximizing impact.
• How can I showcase the case studies created with Junia AI's Case Study Generator? One effective way to showcase the case studies you create with Junia AI's Case Study Generator is by using blog posts. This allows you to reach your target audience through a popular and widely accessible platform, maximizing the visibility of your success stories.
• Does Junia AI's Case Study Generator support collaboration and client sharing? Yes, Junia AI understands the importance of collaboration and client sharing. The Case Study Generator provides easy link sharing options, allowing seamless collaboration between team members and effortless sharing with clients for review and feedback.

We use essential cookies to make Venngage work. By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts.

Manage Cookies

Cookies and similar technologies collect certain information about how you’re using our website. Some of them are essential, and without them you wouldn’t be able to use Venngage. But others are optional, and you get to choose whether we use them or not.

Strictly Necessary Cookies

These cookies are always on, as they’re essential for making Venngage work, and making it safe. Without these cookies, services you’ve asked for can’t be provided.

Show cookie providers

• Google Login

Functionality Cookies

These cookies help us provide enhanced functionality and personalisation, and remember your settings. They may be set by us or by third party providers.

Performance Cookies

These cookies help us analyze how many people are using Venngage, where they come from and how they're using it. If you opt out of these cookies, we can’t get feedback to make Venngage better for you and all our users.

• Google Analytics

Targeting Cookies

These cookies are set by our advertising partners to track your activity and show you relevant Venngage ads on other sites as you browse the internet.

• Google Tag Manager

Free Online Case Study Generator

Captivate your clients by highlighting your company's solutions, and get valuable insights to improve your business strategy with Venngage's case study templates.

Create a case study report that looks compelling and converts leads without any design experience. Showcase real success stories and challenges that your products and services helped to solve. Join over 40,000 businesses in using Venngage as a marketing solution.

Design from one of our case study templates

Choose from hundreds of case study templates. see all case study templates, design professional case studies for meetings, and negotiations.

Create your own case studies to share compelling success stories. Showcase products, strategies, and tactics that had made your clients grow. You don't need any design experience! All of our case study template designs are created in-house by professional designers.

You don't need to be a designer to create a professional-looking case study infographic, or in-depth, multi-page reports. Pick from our library of easy-to-edit case study templates.

Get access to high-quality stock photos and choose from over 40,000+ icons and illustrations to use. Venngage also offers a wide variety of chart and data visualization widgets that you can customize.

Want to incorporate your brand's identity? We've got you. My Brand Kit feature lets you upload your company logos, fonts, and colors. Perfect to stand out in your presentation!

Get your team involved when creating case studies. Real-time collaboration allows you to provide feedback and apply changes creating a great design in minutes.

How to create a case study in 5 easy steps:

Showcase your challenges to elevate your brand with ease

Customizable Templates

No design experience? No problem! Our stunning template designs will make your data analysis look great without even trying.

User-Friendly Editor

Easily visualize and present complex case study examples with Venngage editor. Use our Smart features to quickly add or remove shapes, lines, and branches with a single click.

Access Stunning Photography

A case study report is more engaging and impressive when you use Venngage's library of 3 million stock photos. Professional and royalty-free.

Data Visualization

With Venngage's free case study creator, you can add data collected from a Google Sheet or CSV, and the chart automatically populates the data.

24/7 Customer Support

Experiencing issues? Have questions about using a feature or need advice? Our support team is available around the clock.

My Brand Kit

Build your brand through consistency. My Brand Kit lets you incorporate your branding into every asset you design in Venngage.

Customize Venngage's Case Study Templates

• Choose your favorite design from the templates library. We have an extended gallery of layouts you can work on. Just organize your qualitative and quantitative data, add customizable graphs, icons and images, set your brand identity, and start creating a comprehensive case study report.
• Use illustrations, icons and photos: Case studies ought to be visually engaging and inviting. That's why Venngage lets you access 40K+ icons and beautiful illustrations, impressive stock photos, and customizable charts and graphs.
• Create branded content without any design help: Branded case studies help your brand really stand out. They're an excellent form of lead generation and branding building. Showcase your expertise and real-life success stories that will win over your readers.

Collaborate with team members and stakeholders in real-time

• Replace online meetings, email threads, chats, or messages by simply clicking "Share" from the editor to send a private link to your peers.
• Share your designs, so people can work together and make adjustments to achieve the perfect showcase for your strategies.
• Work better together. Provide feedback, share expertise, and have insights for a perfect process mapping design.

Download and share your case study design with a click

• Download your document as a PDF or Interactive PDF (to use hyperlinking).
• To print your document, apply print bleeds in the editor and then download it as a PDF.
• Share your completed design using a share link - no need to download a single thing.

Great features that make your report stand out

• My Brand Kit lets you instantly apply your branding to any template design, saving you hours of time and effort.
• Access Pixabay, Pexels, and other libraries for impressive stock photos from around the world - for free.
• Hundreds of font options and styles to suit your design preferences. You can also request fonts we don't have to maintain your brand look.

How do I sign up for Venngage's case study creator?

To start using Venngage's free case study creator, sign up for free using your email, Facebook or Gmail account. Once you create an account you can choose which template to get started with and start editing in the online editor.

How do I write a case study?

The easiest way to write a case study is to get started with a template. This provides you with a pre-set cover page and table of contents; a variety of page layouts to work with; and a picture of how to organize content, add designs and break up text. A case study is not a technical document that needs to be structured in a specific and formal way. You can get creative but focus on making your content clear and easy to understand.

What's an example of a case study?

Case studies, in business and marketing, are stories of success achieved through a product or service. The product can even be a strategy or framework that was pioneered by an industry thought leader. Many brands publish case studies on their website to share how their clients see tremendous value in using their products. The case study essentially chronicles the entire client journey from having a problem, to finding the solution, and the outcome of that solution.

Try Venngage's Case Study Creator today. Sign up for free!

Get started with our case study templates:

Business case study, content marketing case study, lead generation business case study, social media case study.

Get an InSimu patient.

Learn teach practice to save lives..

The InSimu Virtual Patient Simulator and Assessment Platform is your partner in assessing real clinical reasoning.

See InSimu in action

Supporting clinical training all around the world.

The most comprehensive virtual patient simulator platform for

Improving clinical reasoning skills.

InSimu is based on a unique algorithm that allows you to practice with an infinite number of virtual patients within the selected sub-specialties. 

Upgrade your learning! Complement your regular quiz-based assessments with the creative and fun method of InSimu and measure your improvement through virtual patients.

• 7500 unique clinical scenarios
• 140+ diseases
• 16 specializations
• 500+ diagnostic tests available
• Cost and time factor included
• ECG and X-ray images included
• Personalized, automated written feedback for each unique case

customize virtual patients. track your group's performance. debrief.

Create clinical courses in 2 minutes.​.

You can easily find and customize the relevant interactive clinical scenarios for your group from the more than 7500 available. By assigning the selected cases as homework, exam, part of OSCE, or as a problem-based session, the robust InSimu Analytics will provide you with an automated evaluation of your gpoups’ performance.

The Unique approach of INsimu

Unified cross-platform experience, start to solve a case on desktop, continue on-the-go with the mobile app..

Open ended simulation

Go beyond multiple choice questions and provide your students with a simulated decision making environment.

7500+ unique cases

With InSimu, you will never run out of new clinical scenarios. With the on-demand patient generator feature you'll find the clinical scenarios you need.

Measure the unmeasurable

Assess real clinical thinking and skills and spot your students strengths and blind spots.

Course creator

Find the relevant clinical scenarios and assign courses in 2 minutes.

Adaptable scenarios

Adapt the optimal diagnostic pathway to local guidelines and set up special clinical context (e.g. the CT is currently not availble).

Educational Assistance

Delegate the technical management of your InSimu clinical courses to the Medical Education Expert Team of InSimu.

InSimu virtual patients covers the 80% of real-life scenarios professionals might face in clinics

Angina pectoris

Aortic aneurysm

Aortic dissection

Arterial thrombosis

Atrial fibrillation

Carbon monoxide poisoning

Infective endocarditis

Myocardial infarction

Pulmonary embolism

Supraventicular tachycardia

Ventricular tachycardia

Acetaminophen poisoning

Alcohol intoxication

Appendicitis

Baker’s cyst

Benign paroxysmal positional vertigo (BPPV)

Budd-Chiari syndrome

Cholecystitis

Crohn’s disease

Deep vein thrombosis (DVT)

Diabetes mellitus

Ectopic pregnancy

Guillain-Barre syndrome

Intracerebral hemorrhage

Mallory-Weiss syndrome

Meningococcal meningitis

Opioid overdose

Pancreatitis

Pneumothorax

Pregnancy (first trimester)

Subarachnoid hemorrhage

Subdural hemorrhage

Swallowed foreign bodies

Vestibular neuronitis

Carcinoid Diabetes mellitus

Hashimoto’s thyroiditis

Hyperparathyroidism

Hyperthyroidism; Graves’ disease

Parathyroid tumor

Pheochromocytoma

Thyroid cancer

Abdominal migraine

Alcoholic liver cirrhosis

Celiac disease

Colorectal carcinoma

Diffuse large B-cell lymphoma

Functional dyspepsia

Gastric cancer

Gastroesophageal reflux disease (GERD)

Gastrointestinal infection

Gastrointestinal stromal tumor (GIST)

Hemochromatosis

Hepatocellular carcinoma (HCC)

Irritable Bowel Syndrome

Lactose intolerance

Nonalcoholic fatty liver disease

Pancreatic cancer

Peptic ulcer

Primer biliary cirrhosis (PBC)

Ulcerative colitis

Wilson’s disease

HIV infection

Multiple sclerosis

Myasthenia gravis

Sjogren’s syndrome

Systemic lupus erythematosus (SLE)

Mononucleosis

Varicella zoster virus infection

Bell’s palsy

Benign paroxysmal positional

Brain tumor

Carpal tunnel syndome

Herniated disk

Migraine headache

Parkinson’s disease

Tension headache

Transient Ischemic Attack (TIA)

Breast cancer

Cervical carcinoma

Endometrial cancer

Ovarian cancer

Bladder cancer

Follicular lymphoma

Hodgkin Lymphoma

Kidney cancer

Laryngeal cancer

Lung cancer

Nasopharyngeal cancer

Oropharyngeal cancer

Prostate cancer

Testicular cancer

Acute lymphoblastic leukemia (ALL)

Pyelonephritis

REINVENT MEDICAL EDUCATION TOGETHER

Join a lively, international medical education community., privacy overview.

Generate Case Studies with ClickUp Brain

We don’t officially support a case study generator in ClickUp right now, but we still offer hundreds of advanced AI use cases with ClickUp Brain. If you think we should add a case study generator, share your feedback here .

Free forever.

No credit card.

What Is A Case Study Generator?

Crafting compelling case studies is now more accessible with AI-powered Case Study Generators. By inputting key details such as industry, challenge, solution, and outcomes, this tool can efficiently produce well-structured case studies. Leveraging natural language processing and data analytics, the AI sifts through vast information sources to generate engaging narratives that resonate with the target audience. This not only saves time on manual writing but also ensures consistency and relevance in storytelling. For businesses aiming to showcase their success stories, attract potential clients, or bolster their brand credibility, utilizing an AI Case Study Generator can be a game-changer in simplifying the content creation process and driving impactful results.

Why ClickUp Brain?

ClickUp Brain is the world's first neural network connecting tasks, docs, people, and all of your company’s knowledge with AI. It’s a knowledge manager, project manager, and writer tailored for the way you work. Use it to Generate case studies

More than 143,000 customers revolutionize their work with ClickUp AI Brain. Boost your team's productivity by 30%, improve alignment across teams, and cut costs by up to 75%.

Teams can save time and stay focused with fewer meetings, quick summaries, and automated tasks. In fact, we find mid-market companies save around $94K per year after cutting unnecessary spend on other AI tools. People across the entire organization feel significantly more connected and aligned on their shared goals.

The days of asking a human are over. ClickUp Brain gives instant, accurate answers based on context from any work within and connected to ClickUp.

Mike Coombe MCM Agency

With the addition of ClickUp AI, I'm more efficient than ever! It saves me 3x the amount of time spent previously on Project Management tasks. Not only has it enhanced my productivity, but it has also ignited my creativity.

3 Use Cases For Case Studies

Marketing team.

The Case Study Generator can be a valuable asset for marketing teams looking to showcase their success stories and client testimonials. By using this tool, marketing teams can easily create compelling case studies that highlight the company's achievements, problem-solving strategies, and the positive outcomes for clients. Generate visually appealing case studies that can be shared across various marketing channels to attract potential customers and build credibility in the industry. Streamline the process of creating impactful case studies and leverage them to drive lead generation and conversions.

Sales teams can benefit from the Case Study Generator by having a repository of persuasive case studies that demonstrate how the company's products or services have addressed specific customer needs and challenges. By using this tool, sales representatives can access a library of success stories that can be personalized and shared with potential clients to build trust and credibility. Tailor case studies to resonate with different industries or target audiences, making it easier to showcase the value proposition of the offerings. Enhance the sales pitch with real-world examples and boost conversion rates with compelling case studies.

Human Resources Department

For the Human Resources department, the Case Study Generator can be a powerful tool for illustrating the company culture, employee development initiatives, and success stories within the organization. Create case studies that highlight employee achievements, career progression, and the impact of training and development programs. Use these case studies for internal communication purposes, employee onboarding, and talent acquisition efforts. Showcase the positive experiences of employees to attract top talent, improve employee engagement, and foster a positive work environment. The Case Study Generator can be a valuable resource for HR teams looking to promote a culture of growth and success within the organization.

Case Study FAQs

What are the key elements to consider when creating a compelling case study for sales.

Key elements to consider when creating a compelling case study for sales include highlighting the customer's challenge or pain point, detailing the solution provided by your product or service, showcasing measurable results or benefits achieved, incorporating direct quotes or testimonials from the customer, and making it visually engaging with graphs, images, and a clear narrative structure.

Where can I find successful examples of sales case studies to learn from?

You can find successful examples of sales case studies to learn from on company websites, industry publications, business school resources, and marketing research websites.

How can a well-crafted case study improve my sales performance?

A well-crafted case study can improve your sales performance by showcasing real-life success stories of satisfied customers who have benefited from your product or service. This provides social proof, builds credibility, addresses common objections, and helps potential customers visualize the benefits and outcomes they can expect, ultimately leading to increased trust, confidence, and conversion rates.

Why ClickUp AI

Learn more from the clickup blog..

How to Be More Organized at Work? Practical Tips and Tricks

7 Client Onboarding Steps That Reduce Churn (With Templates)

11 Effective Ideation Techniques & Methods for Your Team (With Templates!)

One app to replace them all

24/7 support

Weekly updates

Secure and compliant

99.9% uptime

• Affiliate & Referrals
• Product Roadmap
• On-Demand Demo
• ClickUp University
• Consultants
• Gantt Chart
• Native Time Tracking
• Automations
• Integrations
• Kanban Board
• vs Airtable
• vs Basecamp
• vs MS Project
• vs Smartsheet
• Software Team Hub

• Get started Get started for free

Figma design

Design and prototype in one place

Collaborate with a digital whiteboard

Translate designs into code

Get the desktop, mobile, and font installer apps

See the latest features and releases

• Prototyping
• Design systems
• Wireframing
• Online whiteboard
• Team meetings
• Strategic planning
• Brainstorming
• Diagramming
• Product development
• Web development
• Design handoff
• Product managers

Organizations

Config 2024

Register to attend in person or online — June 26–27

Creator fund

Build and sell what you love

User groups

Join a local Friends of Figma group

Learn best practices at virtual events

Customer stories

Read about leading product teams

Stories about bringing new ideas to life

Get started

• Developer docs
• Best practices
• Reports & insights
• Resource library
• Help center

Figma Case study templates

Present your project in pre-built editable templates to get you started.

UX Case study template

UX Case Study Template made to help UX Designers create and organize their case study without any struggle.

Case study presentation template

Case study presentation to frame key insights and outcomes.

Long form research case study template with customizable styles.

Design presentation deck

Modern design deck template with multiple sections.

Visual portfolio template

Modern visual portfolio template with 12 column grid and light and dark themes.

Behance Presentation Template

Case study template with multiple components, visual styles and frame sizes.

Case study template

Case study template with pastel style coloring.

Holistic Case Study Template

Case study presentation template complete with project overview, wireframes and key journey insights.

Case study presentation layout for interview

Case study template with multiple app UI screens.

Portfolio UI - Web & Mobile

A portfolio UI for designers and developers which has 4 unique pages includes blog, case study.

Apple device mockups

Complete Apple device mockup screens for iPhone, Mac, iPad and Apple Watch.

Explore 1,000+ templates on the Figma community

Explore even more templates, widgets, and plugins—all built by the Figma community.

Welcome To The Bluleadz Case Study Generator

This interactive tool is meant to walk you through four simple steps to create a case study specific to your company. Within as little as two minutes, you will build a stylized, unique case study that highlights your best success story.

Let's Get Started

Let's Get Going!

Tell us a bit about yourself, the introduction.

• Self-Employed
• 1 - 10 employees
• 11 - 50 employees
• 51 - 200 employees
• 201 - 500 employees
• 501 - 1000 employees
• 1001 - 5000 employees
• 5001 - 10000 employees
• Over 10000 employees

Next: The Challenge

The Challenge

500 Characters Left

Next: The Solution

The Solution

Next: The Result

Next: An Impact Quote

Impact Quote

150 Characters Left

Next: Customize It

Your Case Study Is Ready!

Company Size

51-100 Employees

Client Location

What was your client’s main challenge prior to utilizing your solution?

What solution did you provide and how did your solution address your client’s key challenge, what goals were achieved when your client used your solution, what are some measurable impacts your client saw after using your solution, impact quote:.

Bluleadz Case Study Generator    •    https://www.bluleadz.com/case-study-generator    •    © 2024 bluleadz.com

Number of Employees

Measurable Impacts

Redefining Content Strategy: The Rise of AI in Case Study Production

In content marketing, case studies are one of the most effective methods of demonstrating a brand's capability and dedication. They illustrate how a brand's products or services come to the rescue in real-world scenarios and highlight your customers' challenges, the strategies used to overcome these challenges, and the results.

Crafting these case studies has traditionally been a pretty detailed task that requires a lot of time and resources. However, with the rise of technology and digital tools, creating impactful case studies has become much easier and more efficient.  AI-powered marketing tools  simplify the process, speed up initial drafting, and provide valuable insights for strategy refinement.

While AI tools seem  unlikely to completely replace human expertise  in marketing and generating content, they certainly have the potential to enhance and optimize the process. One recent analysis has even suggested a remarkable  66% average productivity increase  for businesses using AI tools.

Scripted, an established leader in the content realm, is at the forefront of this shift. Our AI-driven case study tool seamlessly blends technology with traditional storytelling. It allows your business to document and share its success stories efficiently without sacrificing authenticity or emotional connection.

Harnessing the Potential of Scripted's AI-Powered Case Study Tool

Scripted's AI-enhanced case study generator stands out not merely as a tool but as a revolutionary asset. By harnessing advanced algorithms and tapping into a rich database of industry data, Scripted crafts case studies that are both compelling and accurate. Brands no longer need to grapple with the demanding task of creating conventional case studies.

Instead, you can use AI with Scripted to share your successes, highlight your expertise in the industry, and share what sets you apart. With Scripted's AI, you'll have accurate, compelling case studies in a fraction of the usual time.

Understanding Case Study Generators: Why AI?

In today's digital arena, merging artificial intelligence with human subject matter experts (SMEs) seems like a natural evolution. Historically, case studies involved labor-intensive, hands-on research. Now, this domain is on the cusp of transformation led by AI. So, what's propelling this shift, and why does AI emerge as a player in case study creation?

The Essence of Case Studies in the Business World

Case studies give a real-world glimpse of what a company's products or services can do. They're not just simple stories; they paint a clear picture of a brand's ability to tackle challenges, provide meaningful solutions, and showcase its unique strengths.

Far from mere promotional content, a well-structured case study validates a business's skill set, dedication, and customer-focused ethos. It provides potential clients with a vivid depiction of the possibilities when they opt for a specific brand or offering.

A case study is not just a recount of a challenge and resolution. It also connects emotionally with its audience, building trust and establishing legitimacy.

Hurdles in Conventional Case Study Development

Crafting a conventional case study is far from straightforward. It kicks off by pinpointing a significant success narrative, followed by comprehensive interviews, data assimilation, and thorough scrutiny. Once the foundational elements are set, the task is to mold this data into an absorbing tale that remains accurate yet captivating. The soul of a case study resides in its genuineness and precision, leaving minimal wiggle room for inaccuracies.

But the challenges don't stop there. With the demand for new content, companies are pushing to release numerous case studies to highlight the diverse aspects of their services. The lengthy creation process for each case study limits content production, making it challenging for firms to roll out high-caliber case studies on tight schedules. This age-old methodology, while comprehensive, often poses challenges for enterprises seeking a balance of excellence and volume.

AI Innovations: Ushering a New Age in Content Creation

Artificial Intelligence (AI) has been at the forefront of breakthroughs in various fields, and content development is no exception. With its advanced algorithms, AI can seamlessly take on the extensive data collection, organization, and analysis tasks that typically require significant human involvement. This streamlined process significantly speeds up the creation process while maintaining accuracy and authenticity.

Moreover, AI-powered tools can also generate visually appealing infographics to supplement case studies, target specific audiences and demographics, incorporate standards from various relevant industries, and personalize the content for individual clients. The result is a well-rounded, high-quality case study that can be produced at a much faster pace.

Key Considerations

There are many key considerations and questions to consider when incorporating AI solutions into the case study creation process. From assessing the reliability of AI-generated content to understanding its customization capabilities, here are some essential factors to consider:

How Does It Work?

Think of Scripted's case study generator as a smart helper. Just like our  press release generator , it uses AI to sift through lots of data, find important points, and create top-notch content. You simply plug in the key details about your project and its outcomes, and the tool takes care of making a case study that speaks to your industry and audience.

How Long Does It Take?

A typical case study, when done manually, can take anywhere from a week to a month, considering the data collection, writing, and editing processes. With Scripted’s case study generator, you can have a draft ready in mere minutes.

Is It Customizable?

Scripted stands out for its adaptability in its content generation suite. While many AI tools provide templated solutions, Scripted prides itself on its customization capabilities. The platform is intuitive, allowing users to specify their requirements in detail. This ensures the generated content aligns perfectly with your vision.

Whether it's adjusting the tone, style, or structure, Scripted offers a level of flexibility often lacking in other AI tools. This adaptability guarantees that businesses receive a tailored piece of content that fits their unique brand narrative rather than a one-size-fits-all solution.

When you add access to a team of SMEs — professional writers and editors — Scripted's case study generator becomes the ideal solution for businesses looking to save time without compromising quality.

Can It Handle Technical Subjects?

Case studies often contain technical information that can be challenging to convey clearly and concisely. This is where AI-generated content can have a significant impact. Scripted's case study generator uses advanced algorithms and natural language processing (NLP) to accurately represent even the most complex data points.

The platform also allows manual input, giving users the option to add their expertise and domain-specific knowledge to the generated content. This ensures technical subjects are accurately and effectively communicated without losing important details. As with any content, it's always a good idea to have an SME review and approve the final product.

Leading AI Case Study Tools: A Comparative Analysis

A variety of  AI marketing  tools have recently surfaced in the world of AI-driven content creation, each promising to redefine the art and science of case study production. As businesses seek to harness the potential of these tools, understanding their distinct features, strengths, and limitations becomes increasingly important.

In this comparative analysis, we'll explore some of the leading platforms in the industry.

A distinguishing feature of Cohesive is its hybrid functionality, which seems to borrow elements from both ChatGPT and Slack. This combination enables efficient team communication within the platform, which is critical for cohesive case study development. A centralized collaboration means insights, objectives, and data points can be discussed and refined in real time. This ensures that AI-generated case studies have depth and reflect the team's understanding.

Cohesive has a free pricing tier that generously allows unlimited word generation. However, it's limited to only 15 template runs (the number of unique files you generate on Cohesive) per month. For those who require more, it also offers the Creator plan at $25 per month with 150 monthly template runs and the Agency plan at $45 per month with 300 monthly template runs.

On the flip side, the pricing model of the platform could be a hurdle for some. While StoryDoc offers a 14-day free trial to get a feel for its functionalities, it doesn't offer an ongoing free version. A subscription is necessary after the trial, with plans starting at $30 per month for each user. This might be a concern for budget-conscious businesses. However, for those intent on a dynamic, AI-powered method for crafting case studies, the value StoryDoc offers might outweigh the cost.

Best known for its project management prowess,  ClickUp  has made a notable stride into the AI-aided content domain, particularly with its case study generator. Drawing on its foundational strengths in organization and structure, ClickUp equips users with a methodical framework that helps craft detailed, compelling case studies.

ClickUp allows you to weave content creation into project timelines, which supports uniformity, consistency, and speed. Teams across various departments — marketing, sales, product innovation, and customer relations — can use ClickUp to drive productivity. The all-in-one hub approach eliminates the need for multiple apps, encourages collaboration, and drastically cuts down on content creation time.

While ClickUp presents an extensive suite of features, it feels more like a generalist than a specialist in AI content generation. Some of its functionalities might not match the depth or specificity of other available tools, depending on your needs. Also, its free offering is a tad restrictive, capping storage at 100MB and offering limited free use of the AI generator. On the upside, ClickUp's paid versions are reasonably priced, starting at a modest $5 per user monthly.

Grammarly , a titan in content editing and proofreading, has broadened its horizons with the introduction of an AI-driven case study generator nestled within the GrammarlyGO package. With this new feature, users can easily create organized drafts. All you need to do is provide basic information about your business or product. Then, describe the challenges you hope to resolve for customers, mention who these customers are, and explain how you plan to help them.

Some customers complain about  Grammarly's problems  with grasping the deeper nuances of writing. One reviewer says its "recommendations are sometimes not appropriate." Potential users should also be aware that there are prompt limitations — 500 monthly for free users and 1,000 for those on the premium plan.

Though GrammarlyGO offers a fresh avenue for short case-study drafting, its proficiency with longer content and response time leave room for improvement. Another point of consideration is that several platforms easily detect its content as AI-generated. As it stands, GrammarlyGO is a useful addition for those seeking short content improvements, but it may not be the go-to for every user.

Scripted  offers a distinctive blend in AI case-study generation by merging human expertise with AI efficiency. Users are guided through a simple process to produce an AI-generated case study tailored to their specific product, industry, and type of customer. Creating these case studies is free for all users on the platform and can be created with the following steps:

• Begin with Basics : Share what you're all about - your company, product, or interesting project you've worked on.
• The Hurdle : Every story has a challenge. What problem or issue did you tackle?
• Solution Offered : Outline the product or service you implemented to address that challenge.
• Outcomes Achieved : Mention the benefits or positive results that emerged from your solution.
• Size It Up : How lengthy do you want this case study to be? Decide on the word count.
• Industry Category : Specify the industry related to your case study.
• Target Audience : Define the particular demographic or customer group you're addressing.
• Additional Details : If there are other relevant specifics or context that would enrich the case study, include them.

While users can quickly generate content using Scripted's free AI tools, you also have the opportunity to elevate your content by collaborating with Scripted’s subject matter expert writers and editors. This fusion of AI speed and human creativity provides brands with content that not only tells a story but also engages readers.

Users looking to work with Scripted's SMEs can opt for various plans, starting with a pro plan at $199 per month and extending to specialized  team , enterprise, and agency accounts. Depending on your plan, you'll also gain access to expert marketing specialists to help you with your  SEO content strategy,  along with a dedicated account manager.

On top of all this, both  free and paid plan members  gain access to the following array of tools at no additional cost:

• Infographic text generator
• Scripted's GPT-4-powered chatbot,  Scout
• Social media post generator
• Blog idea generator
• Headline generator
• Landing page generator

With such a robust set of tools, it's clear that Scripted is not just a platform for generating content. Scripted is a comprehensive content marketing toolkit that can help businesses of all sizes and industries streamline their content creation process and reach their target audience effectively.

Taking the Plunge With Scripted

In today's digital age, where content reigns supreme, having a trusted ally in content creation becomes indispensable. Scripted, with its blend of cutting-edge AI technology and the finesse of expert writers, offers businesses a unique pathway to engaging, impactful content. Its AI case study generator is more than just a tool. It's a testament to the future of content creation, where technology and human touch work together seamlessly.

When considering a robust, adaptable, and efficient solution for case study creation, Scripted stands out. Its diverse range of templates, the precision of its algorithms, and the invaluable human expertise available ensure that businesses always put their best foot forward. Looking ahead to recognize the direction in which content creation is heading, the choice becomes clear. Dive deep into the powerful combination of  AI-generated content  and human expertise that only Scripted offers.

Ready to redefine your content journey? Discover the unparalleled offerings of Scripted's AI tools and  elevate your case study game today .

Sign Up For Your 30 Day Free Trial Today!

Content Writing How to use AI in Medical Writing [Complete Guide]

• 1) Simplify Complex Terminology:
• 2) Adapt Content Tone for Different Audiences:
• 3) Format Citations According to Guidelines:
• 4) Combine Ethics with Clear Communication:
• 5) Organize Information Logically:
• 6) Enhance Understanding of Medical Concepts:
• 7) Use AI To Summarize Long Medical Studies:
• 8) Use AI to Enhance Grammar and Style:
• 9) Gain Scientific Precision and Simplicity:
• 10) Craft Approachable Patient Communications:
• 11) Maintain Objective Tone and Language:
• 12) Refine Content with AI-Powered Editing:
• 13) Ensure Consistent Terminology:
• 14) Get Contextual Vocabulary Suggestions:
• 15) Automate Citation and Reference Formatting:
• 16.1) Identify Your Objectives:
• 16.2) Know Your Audience:
• 16.3) Customize AI Suggestions:
• 16.4) Combine Human Expertise with AI:
• 16.5) Stay Updated with AI Advances:
• 16.6) Experiment with AI-Generated Outlines:
• 16.7) Proofread with AI Assistance:
• 16.8) Run Originality Checks:
• 16.9) Fact-Check and Verify Data:
• 16.10) Rework Attention-Grabbing Headlines:
• 16.11) Engage in AI-Enhanced Research:
• 16.12) Use AI for Multilingual Content Carefully:
• 16.13) Stay Ethical and Responsible:
• 17) Non-Medical Writers Come Across Several Medical Writing Challenges
• 18) The Solution: Use AI in Medical Writing

Are you struggling to make sense of all those fancy medical words? Medical writing can be like cracking a secret code that only doctors seem to understand. But guess what? You’re not alone. If you’re not a medical expert, these challenges can feel like trying to navigate a maze blindfolded. Solution? Use AI in medical writing.

Imagine trying to explain complex medical stuff in a way that doesn’t make people’s heads spin. It’s like trying to speak a foreign language without ever learning the words. Plus, getting things wrong? That’s like walking on thin ice.

Use Our AI Medical Writing Guide – your trusty sidekick in AI powered medical writing. From tackling those research papers that feel like an uphill battle to making doctor and layman friendly, you will find some great tips on how to Use AI in medical writing.

Let’s see some helpful tips on how to use AI in medical writing:

Simplify Complex Terminology:

Ever read something and felt like you stumbled into a maze of medical words? AI tools can help. They take those tongue-twisting terms and swap them for plain language, so you’re not left scratching your head. You get the info without needing a medical dictionary.

Reusable AI Medical Writing Prompt:  

 “Provide simpler alternatives for ‘myocardial infarction’ in patient-focused content, maintaining accuracy.”

Output: 

Sure, simpler alternatives for “myocardial infarction” can be “heart attack” or “blockage in the heart”. Both of these terms are accurate and easier for a patient to understand.

Adapt Content Tone for Different Audiences:

Imagine writing for doctors and your neighbor. That’s a balancing act, but AI can help. It tweaks your words to fit the crowd you’re talking to, making sure your message resonates whether you’re addressing pros or just regular folks.

 “Adapt the language about [medical procedure] for a general audience while retaining medical accuracy.”

Format Citations According to Guidelines:

Citations – the bane of every writer’s existence. But guess what? AI can do the heavy lifting. Tell it the style you need, and bam! Your references are neatly organized, and you’re spared the headache.

“Format the references in this content according to the AMA style guidelines [insert content].”

Combine Ethics with Clear Communication:

Talking about medical stuff isn’t just about facts; it’s about feelings too. AI checks your content for both – making sure your words are sensitive and accurate. It’s like having an empathy filter for your writing.

“Review this content discussing [treatment/condition] for empathetic language and medical accuracy. [insert content]”

Organize Information Logically:

Ever read something that jumps all over the place? AI helps you avoid that. It lays out your ideas in a logical sequence, so your readers don’t feel like they’re on a roller coaster of information.

“Generate an outline based on the research on [insert medical topic] to help structure my article.”

Enhance Understanding of Medical Concepts:

You know those terms that sound like gibberish? AI breaks them down for you. It explains stuff in everyday language, so you’re not lost in a sea of medical mumbo-jumbo.

 “Explain the term ‘diabetes mellitus’ in simple language suitable for a general audience.”

Use AI To Summarize Long Medical Studies:

Imagine you’re on a mission to stay hip with the latest medical marvels. AI writing tools slide in like your trusty research partner. They dive into those hefty research papers, sieve out the juicy nuggets – the big discoveries, the methods that matter, and the outcomes that count. So, you’re up to date without feeling like you’re drowning in paper seas.

Expert Tip: Use AI to summarize research papers and quickly identify relevant information. Combine summaries from different tools to create a comprehensive overview.

 “Summarize this research paper on [topic] to highlight the key findings, methods, and results. [insert research paper content]”

Use AI to Enhance Grammar and Style:

Think about it: you’re crafting a case study that’s got to be both brainy and breezy. That’s where AI tools step in, your language wizards. They catch those tangled sentences and serve up smoother options. Your medical expertise flows smoothly, even for folks who aren’t experts.

Expert Tip: Utilize AI grammar tools, but also customize their suggestions to match medical writing conventions. Regularly review and adapt these suggestions to create a consistent medical writing style.

“Review [medical case study] for complex sentence structures and suggest simplifications while maintaining medical accuracy. [insert case study]”

Gain Scientific Precision and Simplicity:

Picture this: you’re explaining a medical concept, and you want it to make sense to everyone. AI comes to the rescue, making sure you don’t lose the juicy details while keeping things simple. It’s like having a science translator that keeps it real.

Review my explanation of [specific medical concept] and suggest ways to simplify it without losing important details. [insert your explanation]”

Craft Approachable Patient Communications:

Explaining medical things to patients isn’t easy. But AI makes it a breeze. It helps you turn complex info into friendly chats, so patients can make informed decisions without feeling lost.

“Offer patient-friendly explanations of [medical procedure/condition] for a brochure targeting non-medical readers.”

Maintain Objective Tone and Language:

 “Review [content] to ensure it remains objective, with no personal opinions or biases. “

Refine Content with AI-Powered Editing:

Ever wished for a writing assistant that polishes your work? AI’s got you. It fine-tunes your writing, making it shine like a pro did the editing.

 “Edit [ article] according to medical writing best practices to enhance clarity and accuracy [insert article].”

Ensure Consistent Terminology:

Ever been lost in a maze of words? AI tools become your terminology detectives. They nab those sneaky word switches, making sure your team’s masterpiece speaks the same medical language. It’s like teamwork on the same musical note – smooth, harmonious, and crystal clear.

Expert Tip: Create a glossary of standard medical terms and phrases. Use AI tools to cross-reference your writing against this glossary, ensuring consistent usage throughout.

 “Check this manuscript for variations in medical terminology and suggest corrections to maintain uniform language. [insert manuscript]”

Get Contextual Vocabulary Suggestions:

Now, let’s talk about breaking things down for everyday folks. AI writing tools become your translation buddies. They swap complex medical words for simpler ones, turning your patient education materials into a friendly chat. No more jargon confusion – just clear understanding.

Expert Tip: Use AI tools to recognize medical jargon and suggest layman-friendly alternatives. Test these suggestions with non-expert readers to ensure clarity without compromising accuracy.

“Suggest simpler alternatives for [complex medical terms] in [patient education document] while retaining the accuracy of information.”

Automate Citation and Reference Formatting:

You’re diving into the research jungle, but then come the citation monsters. But wait, AI tools to the rescue! They swoop in as your citation superheroes. They gather your sources, weave them into the right format, and voilà – your references are a neat and tidy bunch, while you focus on the meaty stuff.

Expert Tip: Regularly update your AI tool’s reference styles based on journal guidelines. Manually check a sample of generated citations to ensure accuracy.

“ Generate citations and compile a bibliography in AMA format for the references in [research paper].”

Make Content Globally Accessible through AI Translation:

You’re sharing medical wisdom, but languages stand in the way. Enter AI-powered translation tools, your linguistic pals. They take your work and shape it into different languages, so your insights dance across borders and break through language barriers. It’s like having a global megaphone for your expertise.

Expert Tip: While AI tools can aid in translation, engage medical professionals fluent in the target language to review and refine the translated content for accuracy.

“Translate [patient brochure content] from [English] to [target language], ensuring that medical terminology is accurately conveyed

Additional Tips for Using AI in Medical Writing

Identify your objectives:.

Before diving into AI tools , clarify your writing goals. Are you creating patient education content, research summaries, or opinion pieces? Different tools may be more suitable for each purpose, so align your tool choice with your content type.

Know Your Audience:

Understand who you’re writing for. AI tools can help simplify complex terms, but for medical professionals, the jargon might be necessary. For patient-facing content, AI can aid in keeping it approachable and understandable.

Customize AI Suggestions:

AI tools provide suggestions, but don’t treat them as gospel. Tailor these suggestions to your unique writing style. For instance, if you often use specific medical acronyms, train the AI to recognize and appropriately incorporate them.

Combine Human Expertise with AI:

While AI tools enhance efficiency, remember that they lack human intuition. Always review and refine AI-generated content to ensure it aligns with medical accuracy and context. Your expertise is irreplaceable.

Stay Updated with AI Advances:

AI writing tools continuously improve. Stay informed about updates and new tools specifically designed for medical writing. Regularly explore emerging options that might better suit your evolving needs.

Experiment with AI-Generated Outlines:

Consider letting AI generate an outline for your medical blog post. It can analyze your research and suggest a structure that ensures you cover all crucial aspects of the topic. This can be a time-saving starting point for your writing.

Proofread with AI Assistance:

While you’re a medical expert, AI tools are grammar helpers. Use them to meticulously proofread your articles, catching those tricky typos and grammatical errors that might slip through your medical-focused review.

Run Originality Checks:

Imagine you’re piecing together a massive medical puzzle from all over. AI-powered originality checks? They’re your puzzle guardians. They scan your work against the whole world’s knowledge, making sure your brilliance shines, without any accidental copy-paste hiccups.

Expert Tip: Before submission, run your manuscript through multiple plagiarism detection tools. Cross-reference results to ensure that your work is entirely original and properly attributed.

Fact-Check and Verify Data:

AI tools can assist in cross-referencing data within your articles. However, verify critical medical information from reputable sources independently to ensure your content is reliable and trustworthy.

Rework Attention-Grabbing Headlines:

Experiment with AI-generated headlines to pique reader interest. However, don’t solely rely on them; leverage your medical insights to ensure that the headline accurately reflects the content’s value.

Engage in AI-Enhanced Research:

Use AI-powered research tools to scour medical databases for relevant studies. These tools can save you time by sifting through vast amounts of literature and delivering the most pertinent information.

Use AI for Multilingual Content Carefully:

If you’re catering to an international audience, leverage AI translation tools to offer your content in multiple languages. However, ensure translations are reviewed by medical experts to preserve accuracy.

Stay Ethical and Responsible:

When using AI tools to generate content, give proper attribution for AI-generated portions, especially in medical writing where you are using AI.

Non-Medical Writers Come Across Several Medical Writing Challenges

• Navigating Complex Medical Terminology.
• Conveying Scientific Concepts in Simple Language.
• Summarizing Extensive Research Papers Succinctly.
• Adapting Tone for Different Audience Levels.
• Formatting Citations According to Guidelines.
• Balancing Ethical Considerations and Clarity.
• Structuring Information Logically and Coherently.
• Explaining Medical Concepts to Non-Experts.
• Staying Current with Rapid Medical Advancements.
• Crafting Patient-Friendly Communications.
• Maintaining Objective and Unbiased Content.
• Ensuring Accurate and Well-Edited Writing.

The Solution: Use AI in Medical Writing

Simplify the overwhelming task of medical writing and use AI writing tools to write medical content like a pro. AI in Medical writing unlocks new avenues of scientific precision and tone accuracy for your audience, so you can establish your authority in the medical niche. 

Recommended Reads:

• Writing Coherence and Readability – How to Humanize AI Writing? [Complete Guide]
• Complete Guide to “Fixing” AI Generated Content
• 11 Ways to Use AI To Improve Writing – Level The Field with Native Writers!
• How to Write a Tagline? Top Writing Tips

Recent Posts

• Applications of Generative AI in Healthcare – Uses and Challenges
• AI Powered B2B Lead Generation [2024]
• What is Content Automation And Which Tools are Best for It?
• AI Personalized Content: Beginner’s Guide & Prompts
• What is AI as a Service? Definition, Types, Benefits & Trends
• Affiliate Marketing
• Artificial Intelligence
• Content Scaling
• Business Pitch Deck
• Copywriting
• Newsletters
• Product Description
• Social Media
• Songwriting
• Title and Headline
• Writing Ideas
• Call to Action (CTA)
• Email Marketing
• NLP (Natural Language Processing)
• Proposal Writing
• Writer's Block Problem

Email Address

• « AI Content Summarization: Write Less And Convey More
• 4 Recommended AI Writing Tools for Creating Marketing Content in 2024 »
• Privacy Policy
• Terms of Use
• Chat with AI
• AI Email Generator
• Social Media Ads Generator
• Blog Idea Generator
• Blog Outline Generator
• Complete Article Generator
• All use cases >
• Bloggers & Vloggers
• Digital Marketing Agency
• Local Stores
• Freelancers
• All industries >
• Affiliate Program
• Comparisons
• System Status

Get in Touch

Case Study Writer

General Information Case Format Customer Feedback and Complaints: Receiving feedback, handling complaints Government Services: Information on government policies, documentation Travel and Tourism: Travel advice, booking assistance, tourism services Event Information: Details on events, registration, schedules Environmental and Sustainability Issues: Information on environmental initiatives

General Research Case Format Legal Consultation: Legal inquiries, document assistance Health and Medical Advice: Medical symptoms, health questions Educational Guidance: Information on courses, academic advice Community Services and Social Work: Social services, community aid

Individual Perspective Case Format Personal Account Management: Issues related to account settings, privacy Subscription Services: Managing subscriptions, renewals, cancellations Safety and Emergency: Urgent assistance, safety information

Generating your Case Prompt and Custom Prompts, please be patient. This can take a few moments.

**NEW** – Beta Custom Prompts

These are 10 custom prompts that you can use based on the prompt you generated. Its unique and yours. These prompts are only in Beta so you can not change or edit BUT, copy it and paste the case in ChatGPT to modify them.

Write a Results-Based Case Study in a Few Minutes with AI

Effortlessly craft captivating stories that showcase your customer triumphs and illustrating the impactful solutions you provide.

Compose a polished, professional case study in minutes. Our AI tool, powered by OpenAI GPT-4, simplifies the process with an intuitive fill-in-the-blank form, eliminating the struggle with complex chatbots.

Stay connected with your guests from the moment they book until they check out. Our tool provides you with templates for booking confirmations, check-in details, and check-out procedures, ensuring smooth communication at every stage.

No more generic welcome booklets. Dive into our tailored content suggestions to create a welcome book that's both informative and personal. Make your guests feel right at home, with all the essential information they need at their fingertips.

Introducing the Ultimate Content Optimizer for Short-Term Rental Owners!

Why is Better Copy Crucial for Your Rental Business?

• Set Clear Expectations : Avoid misunderstandings by ensuring your guests know exactly what to expect.
• Reduce Unpleasant Surprises : With clear and concise communication, you minimize the chances of last-minute hiccups.
• Enhanced Guest Experience : When guests feel taken care of and informed, they’re more likely to enjoy their stay.
• Handle Difficult Conversations with Ease : Our tool helps you navigate tricky situations, allowing you to remain assertive without appearing rude.

The Result? Happier guests and glowing reviews. In the competitive world of short-term rentals, your content can make all the difference. Improve guest satisfaction and watch your ratings soar!

Invest in your business’s success. Let our SaaS tool transform your content, and in turn, your guest’s experience. Step up your rental game with content that counts!

Tortor egestas tortor sed sagittis in sagittis nunc cursus. Adipiscing sit blandit arcu congue porttitor.

Ad Copywriter

Generate, tweak, and regenerate compelling ad copy in seconds with this AI powered ad writing tool.

• Article Writer

Generate tailored articles for any topic, audience, and tone. Receive thorough drafts in a flash. Optimize results with descriptive inputs. Refine and regenerate to perfection.

Brand Name Brainstormer

Brainstorm a dozen captivating business name ideas in less than a minute. Enter your industry and details to inspire an AI name-storming session!

Brand Voice Analyzer

Paste your copy to get a description of your brand voice as well as a custom prompt to help you recreate it.

Brand Voice Guidelines

Prepare a guide to help ensure consistent and clear communication across brand touch points

A suite of specialized AI tools that write, brainstorm, fine-tune, and unlock new creative superpowers.  Subscribe to get full access to our entire library of tools. 

Word.Studio is LLM agnostic, which means that our tools are run by the most powerful and appropriate AI models selected for the task at hand.

• Cover Letter Writer
• Freelancer Bio Writer
• Blog Post Writer
• Professional Bio Writer
• Glossary Generator
• Business Tools
• Creative Tools
• Writing Tools
• Career & Job Search
• Hiring & Recruiting
• Sales Tools
• Teaching Tools
• Storytelling Tools
• Word.Studio Tools
• Articles & Tutorials
• GPT Directory
• Prompt Templates
• AI Glossary
• AI Tool Development
• Custom GPT Development
• Terms of Service
• Privacy Policy

Word.Studio 2024 - Terms & Privacy

• Open access
• Published: 22 April 2024

Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study

• Kannan Sridharan 1 &
• Reginald P. Sequeira 1  

BMC Medical Education volume  24 , Article number:  431 ( 2024 ) Cite this article

2 Altmetric

Metrics details

Artificial intelligence (AI) tools are designed to create or generate content from their trained parameters using an online conversational interface. AI has opened new avenues in redefining the role boundaries of teachers and learners and has the potential to impact the teaching-learning process.

In this descriptive proof-of- concept cross-sectional study we have explored the application of three generative AI tools on drug treatment of hypertension theme to generate: (1) specific learning outcomes (SLOs); (2) test items (MCQs- A type and case cluster; SAQs; OSPE); (3) test standard-setting parameters for medical students.

Analysis of AI-generated output showed profound homology but divergence in quality and responsiveness to refining search queries. The SLOs identified key domains of antihypertensive pharmacology and therapeutics relevant to stages of the medical program, stated with appropriate action verbs as per Bloom’s taxonomy. Test items often had clinical vignettes aligned with the key domain stated in search queries. Some test items related to A-type MCQs had construction defects, multiple correct answers, and dubious appropriateness to the learner’s stage. ChatGPT generated explanations for test items, this enhancing usefulness to support self-study by learners. Integrated case-cluster items had focused clinical case description vignettes, integration across disciplines, and targeted higher levels of competencies. The response of AI tools on standard-setting varied. Individual questions for each SAQ clinical scenario were mostly open-ended. The AI-generated OSPE test items were appropriate for the learner’s stage and identified relevant pharmacotherapeutic issues. The model answers supplied for both SAQs and OSPEs can aid course instructors in planning classroom lessons, identifying suitable instructional methods, establishing rubrics for grading, and for learners as a study guide. Key lessons learnt for improving AI-generated test item quality are outlined.

Conclusions

AI tools are useful adjuncts to plan instructional methods, identify themes for test blueprinting, generate test items, and guide test standard-setting appropriate to learners’ stage in the medical program. However, experts need to review the content validity of AI-generated output. We expect AIs to influence the medical education landscape to empower learners, and to align competencies with curriculum implementation. AI literacy is an essential competency for health professionals.

Peer Review reports

Artificial intelligence (AI) has great potential to revolutionize the field of medical education from curricular conception to assessment [ 1 ]. AIs used in medical education are mostly generative AI large language models that were developed and validated based on billions to trillions of parameters [ 2 ]. AIs hold promise in the incorporation of history-taking, assessment, diagnosis, and management of various disorders [ 3 ]. While applications of AIs in undergraduate medical training are being explored, huge ethical challenges remain in terms of data collection, maintaining anonymity, consent, and ownership of the provided data [ 4 ]. AIs hold a promising role amongst learners because they can deliver a personalized learning experience by tracking their progress and providing real-time feedback, thereby enhancing their understanding in the areas they are finding difficult [ 5 ]. Consequently, a recent survey has shown that medical students have expressed their interest in acquiring competencies related to the use of AIs in healthcare during their undergraduate medical training [ 6 ].

Pharmacology and Therapeutics (P & T) is a core discipline embedded in the undergraduate medical curriculum, mostly in the pre-clerkship phase. However, the application of therapeutic principles forms one of the key learning objectives during the clerkship phase of the undergraduate medical career. Student assessment in pharmacology & therapeutics (P&T) is with test items such as multiple-choice questions (MCQs), integrated case cluster questions, short answer questions (SAQs), and objective structured practical examination (OSPE) in the undergraduate medical curriculum. It has been argued that AIs possess the ability to communicate an idea more creatively than humans [ 7 ]. It is imperative that with access to billions of trillions of datasets the AI platforms hold promise in playing a crucial role in the conception of various test items related to any of the disciplines in the undergraduate medical curriculum. Additionally, AIs provide an optimized curriculum for a program/course/topic addressing multidimensional problems [ 8 ], although robust evidence for this claim is lacking.

The existing literature has evaluated the knowledge, attitude, and perceptions of adopting AI in medical education. Integration of AIs in medical education is the need of the hour in all health professional education. However, the academic medical fraternity facing challenges in the incorporation of AIs in the medical curriculum due to factors such as inadequate grounding in data analytics, lack of high-quality firm evidence favoring the utility of AIs in medical education, and lack of funding [ 9 ]. Open-access AI platforms are available free to users without any restrictions. Hence, as a proof-of-concept, we chose to explore the utility of three AI platforms to identify specific learning objectives (SLOs) related to pharmacology discipline in the management of hypertension for medical students at different stages of their medical training.

Study design and ethics

The present study is observational, cross-sectional in design, conducted in the Department of Pharmacology & Therapeutics, College of Medicine and Medical Sciences, Arabian Gulf University, Kingdom of Bahrain, between April and August 2023. Ethical Committee approval was not sought given the nature of this study that neither had any interaction with humans, nor collection of any personal data was involved.

Study procedure

We conducted the present study in May-June 2023 with the Poe© chatbot interface created by Quora© that provides access to the following three AI platforms:

Sage Poe [ 10 ]: A generative AI search engine developed by Anthropic © that conceives a response based on the written input provided. Quora has renamed Sage Poe as Assistant © from July 2023 onwards.

Claude-Instant [ 11 ]: A retrieval-based AI search engine developed by Anthropic © that collates a response based on pre-written responses amongst the existing databases.

ChatGPT version 3.5 [ 12 ]: A generative architecture-based AI search engine developed by OpenAI © trained on large and diverse datasets.

We queried the chatbots to generate SLOs, A-type MCQs, integrated case cluster MCQs, integrated SAQs, and OSPE test items in the domain of systemic hypertension related to the P&T discipline. Separate prompts were used to generate outputs for pre-clerkship (preclinical) phase students, and at the time of graduation (before starting residency programs). Additionally, we have also evaluated the ability of these AI platforms to estimate the proportion of students correctly answering these test items. We used the following queries for each of these objectives:

Specific learning objectives

Can you generate specific learning objectives in the pharmacology discipline relevant to undergraduate medical students during their pre-clerkship phase related to anti-hypertensive drugs?

Can you generate specific learning objectives in the pharmacology discipline relevant to undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

A-type MCQs

In the initial query used for A-type of item, we specified the domains (such as the mechanism of action, pharmacokinetics, adverse reactions, and indications) so that a sample of test items generated without any theme-related clutter, shown below:

Write 20 single best answer MCQs with 5 choices related to anti-hypertensive drugs for undergraduate medical students during the pre-clerkship phase of which 5 MCQs should be related to mechanism of action, 5 MCQs related to pharmacokinetics, 5 MCQs related to adverse reactions, and 5 MCQs should be related to indications.

The MCQs generated with the above search query were not based on clinical vignettes. We queried again to generate MCQs using clinical vignettes specifically because most medical schools have adopted problem-based learning (PBL) in their medical curriculum.

Write 20 single best answer MCQs with 5 choices related to anti-hypertensive drugs for undergraduate medical students during the pre-clerkship phase using a clinical vignette for each MCQ of which 5 MCQs should be related to the mechanism of action, 5 MCQs related to pharmacokinetics, 5 MCQs related to adverse reactions, and 5 MCQs should be related to indications.

We attempted to explore whether AI platforms can provide useful guidance on standard-setting. Hence, we used the following search query.

Can you do a simulation with 100 undergraduate medical students to take the above questions and let me know what percentage of students got each MCQ correct?

Integrated case cluster MCQs

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students during the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette.

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students during the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette. Please do not include ‘none of the above’ as the choice. (This modified search query was used because test items with ‘None of the above’ option were generated with the previous search query).

Write 20 integrated case cluster MCQs with 2 questions in each cluster with 5 choices for undergraduate medical students at the time of graduation integrating pharmacology and physiology related to systemic hypertension with a case vignette.

Integrated short answer questions

Write a short answer question scenario with difficult questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Write a short answer question scenario with moderately difficult questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Write a short answer question scenario with questions based on the theme of a newly diagnosed hypertensive patient for undergraduate medical students at the time of graduation with the main objectives related to the physiology of blood pressure regulation, risk factors for systemic hypertension, pathophysiology of systemic hypertension, pathological changes in the systemic blood vessels in hypertension, pharmacological management, and non-pharmacological treatment of systemic hypertension.

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises for the assessment of undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises containing appropriate instructions for the patients for the assessment of undergraduate medical students during their pre-clerkship phase related to anti-hypertensive drugs?

Can you generate 5 OSPE pharmacology and therapeutics prescription writing exercises containing appropriate instructions for the patients for the assessment of undergraduate medical students at the time of graduation related to anti-hypertensive drugs?

Both authors independently evaluated the AI-generated outputs, and a consensus was reached. We cross-checked the veracity of answers suggested by AIs as per the Joint National Commission Guidelines (JNC-8) and Goodman and Gilman’s The Pharmacological Basis of Therapeutics (2023), a reference textbook [ 13 , 14 ]. Errors in the A-type MCQs were categorized as item construction defects, multiple correct answers, and uncertain appropriateness to the learner’s level. Test items in the integrated case cluster MCQs, SAQs and OSPEs were evaluated with the Preliminary Conceptual Framework for Establishing Content Validity of AI-Generated Test Items based on the following domains: technical accuracy, comprehensiveness, education level, and lack of construction defects (Table  1 ). The responses were categorized as complete and deficient for each domain.

The pre-clerkship phase SLOs identified by Sage Poe, Claude-Instant, and ChatGPT are listed in the electronic supplementary materials 1 – 3 , respectively. In general, a broad homology in SLOs generated by the three AI platforms was observed. All AI platforms identified appropriate action verbs as per Bloom’s taxonomy to state the SLO; action verbs such as describe, explain, recognize, discuss, identify, recommend, and interpret are used to state the learning outcome. The specific, measurable, achievable, relevant, time-bound (SMART) SLOs generated by each AI platform slightly varied. All key domains of antihypertensive pharmacology to be achieved during the pre-clerkship (pre-clinical) years were relevant for graduating doctors. The SLOs addressed current JNC Treatment Guidelines recommended classes of antihypertensive drugs, the mechanism of action, pharmacokinetics, adverse effects, indications/contraindications, dosage adjustments, monitoring therapy, and principles of monotherapy and combination therapy.

The SLOs to be achieved by undergraduate medical students at the time of graduation identified by Sage Poe, Claude-Instant, and ChatGPT listed in electronic supplementary materials 4 – 6 , respectively. The identified SLOs emphasize the application of pharmacology knowledge within a clinical context, focusing on competencies needed to function independently in early residency stages. These SLOs go beyond knowledge recall and mechanisms of action to encompass competencies related to clinical problem-solving, rational prescribing, and holistic patient management. The SLOs generated require higher cognitive ability of the learner: action verbs such as demonstrate, apply, evaluate, analyze, develop, justify, recommend, interpret, manage, adjust, educate, refer, design, initiate & titrate were frequently used.

The MCQs for the pre-clerkship phase identified by Sage Poe, Claude-Instant, and ChatGPT listed in the electronic supplementary materials 7 – 9 , respectively, and those identified with the search query based on the clinical vignette in electronic supplementary materials ( 10 – 12 ).

All MCQs generated by the AIs in each of the four domains specified [mechanism of action (MOA); pharmacokinetics; adverse drug reactions (ADRs), and indications for antihypertensive drugs] are quality test items with potential content validity. The test items on MOA generated by Sage Poe included themes such as renin-angiotensin-aldosterone (RAAS) system, beta-adrenergic blockers (BB), calcium channel blockers (CCB), potassium channel openers, and centrally acting antihypertensives; on pharmacokinetics included high oral bioavailability/metabolism in liver [angiotensin receptor blocker (ARB)-losartan], long half-life and renal elimination [angiotensin converting enzyme inhibitors (ACEI)-lisinopril], metabolism by both liver and kidney (beta-blocker (BB)-metoprolol], rapid onset- short duration of action (direct vasodilator-hydralazine), and long-acting transdermal drug delivery (centrally acting-clonidine). Regarding the ADR theme, dry cough, angioedema, and hyperkalemia by ACEIs in susceptible patients, reflex tachycardia by CCB/amlodipine, and orthostatic hypotension by CCB/verapamil addressed. Clinical indications included the drug of choice for hypertensive patients with concomitant comorbidity such as diabetics (ACEI-lisinopril), heart failure and low ejection fraction (BB-carvedilol), hypertensive urgency/emergency (alpha cum beta receptor blocker-labetalol), stroke in patients with history recurrent stroke or transient ischemic attack (ARB-losartan), and preeclampsia (methyldopa).

Almost similar themes under each domain were identified by the Claude-Instant AI platform with few notable exceptions: hydrochlorothiazide (instead of clonidine) in MOA and pharmacokinetics domains, respectively; under the ADR domain ankle edema/ amlodipine, sexual dysfunction and fatigue in male due to alpha-1 receptor blocker; under clinical indications the best initial monotherapy for clinical scenarios such as a 55-year old male with Stage-2 hypertension; a 75-year-old man Stage 1 hypertension; a 35-year-old man with Stage I hypertension working on night shifts; and a 40-year-old man with stage 1 hypertension and hyperlipidemia.

As with Claude-Instant AI, ChatGPT-generated test items on MOA were mostly similar. However, under the pharmacokinetic domain, immediate- and extended-release metoprolol, the effect of food to enhance the oral bioavailability of ramipril, and the highest oral bioavailability of amlodipine compared to other commonly used antihypertensives were the themes identified. Whereas the other ADR themes remained similar, constipation due to verapamil was a new theme addressed. Notably, in this test item, amlodipine was an option that increased the difficulty of this test item because amlodipine therapy is also associated with constipation, albeit to a lesser extent, compared to verapamil. In the clinical indication domain, the case description asking “most commonly used in the treatment of hypertension and heart failure” is controversial because the options listed included losartan, ramipril, and hydrochlorothiazide but the suggested correct answer was ramipril. This is a good example to stress the importance of vetting the AI-generated MCQ by experts for content validity and to assure robust psychometrics. The MCQ on the most used drug in the treatment of “hypertension and diabetic nephropathy” is more explicit as opposed to “hypertension and diabetes” by Claude-Instant because the therapeutic concept of reducing or delaying nephropathy must be distinguished from prevention of nephropathy, although either an ACEI or ARB is the drug of choice for both indications.

It is important to align student assessment to the curriculum; in the PBL curriculum, MCQs with a clinical vignette are preferred. The modification of the query specifying the search to generate MCQs with a clinical vignette on domains specified previously gave appropriate output by all three AI platforms evaluated (Sage Poe; Claude- Instant; Chat GPT). The scenarios generated had a good clinical fidelity and educational fit for the pre-clerkship student perspective.

The errors observed with AI outputs on the A-type MCQs are summarized in Table  2 . No significant pattern was observed except that Claude-Instant© generated test items in a stereotyped format such as the same choices for all test items related to pharmacokinetics and indications, and all the test items in the ADR domain are linked to the mechanisms of action of drugs. This illustrates the importance of reviewing AI-generated test items by content experts for content validity to ensure alignment with evidence-based medicine and up-to-date treatment guidelines.

The test items generated by ChatGPT had the advantage of explanations supplied rendering these more useful for learners to support self-study. The following examples illustrate this assertion: “ A patient with hypertension is started on a medication that works by blocking beta-1 receptors in the heart (metoprolol)”. Metoprolol is a beta blocker that works by blocking beta-1 receptors in the heart, which reduces heart rate and cardiac output, resulting in a decrease in blood pressure. However, this explanation is incomplete because there is no mention of other less important mechanisms, of beta receptor blockers on renin release. Also, these MCQs were mostly recall type: Which of the following medications is known to have a significant first-pass effect? The explanation reads: propranolol is known to have a significant first pass-effect, meaning that a large portion of the drug is metabolized by the liver before it reaches systemic circulation. Losartan, amlodipine, ramipril, and hydrochlorothiazide do not have significant first-pass effect. However, it is also important to extend the explanation further by stating that the first-pass effect of propranolol does not lead to total loss of pharmacological activity because the metabolite hydroxy propranolol also has potent beta-blocking activity. Another MCQ test item had a construction defect: “A patient with hypertension is started on a medication that can cause photosensitivity. Which of the following medications is most likely responsible?” Options included: losartan, amlodipine, ramipril, hydrochlorothiazide, hydrochlorothiazide/triamterene. The explanation states that hydrochlorothiazide (HCTZ) can cause photosensitivity, which is increased sensitivity to sunlight that can cause skin rashes and sunburns. Losartan, amlodipine, ramipril, and HCTZ/triamterene are less likely to cause photosensitivity. However, it is irrational to claim that HCTZ/triamterene, one of the wrong options, is less likely to cause photosensitivity. The difficulty index of such test items is likely unacceptable in high-stake tests to make equitable psychometric decisions.

The responses from Sage Poe, Claude-Instant, and ChatGPT on guidance for standard-setting methods are listed in the electronic supplementary materials 13 – 15 . Sage Poe offered this information with the caveat that “the results of this simulation were purely hypothetical and do not reflect the actual performance of any real students”. The simulation estimates of examinees correctly answering test items ranged from 60 to 90% for the mechanism of action, 65–85% for pharmacokinetics, 60–80% for adverse reactions, and 75–90% for clinical indications of antihypertensives.

In contrast, Claude-Instant admitted that “Performing a full simulation with 100 medical students is beyond the scope of what I can do as an AI assistant. I can generate sample MCQ questions and answers, but I do not have access to a population of medical students to administer the questions. A true simulation that administers questions to real students and analyzes the results would require access to a database of medical students, a platform to deliver the questions, and code to analyze the responses. As an AI assistant, I lack these capabilities. While I cannot perform a full simulation, here are some considerations for administering MCQ questions to medical students: provide details on the purpose and instructions for the test to ensure students understand what is expected out of them; consider including some easier questions in addition to more challenging ones to boost students’ confidence and motivation; avoid ambiguous wording and ensure each question has only one unambiguously correct answer option; when analyzing results, look for questions factors that may have impacted performance, like unclear wording or answer options that are too similar. Focusing on weak questions can help improve future tests; compare performance across different student groups to identify areas where more teaching may be needed; provide detailed answer explanations and review sessions after the test to help students learn from any errors and reinforce correct concepts”. However, ChatGPT offered the simulation result estimates for the 20 MCQ on antihypertensive drugs: mechanism of action ranged from 67 to 92%, pharmacokinetics 63–86%, adverse effects 65–82%, and clinical indications 64–89%. Furthermore, it also stated that “Overall, the performance of the students was quite good, with most questions having a response rate of over 70%. However, there were some questions where the projected correct response rate was rather low, such as Question #5 (Mechanism of Action of Hydrochlorothiazide; see Electronic Supplementary Material 12 ) and Question 18 (Indications for Verapamil; see Electronic Supplementary Material 10 ). This may suggest areas where students need more focused education or review.”

We asked AI assistants to generate 20 integrated case cluster MCQs with 2 test items in each cluster with five options for undergraduate medical students in the pre-clerkship phase integrating pharmacology and physiology related to systemic hypertension with a case vignette and the responses by Sage Poe, Claude-Instant, and ChatGPT are listed in the electronic supplementary materials ( 16 – 18 ). In all instances, the test items generated had focused case descriptions in the form of a clinical vignette, and horizontal integration across the pathophysiology of hypertension and pharmacology of antihypertensive drugs. These test items mostly targeted the ‘knows (knowledge)’ or ‘knows how (competence)’ level on Miller’s pyramid and are suitable for assessing the clinical competence of pre-clerkship medical students, especially in an integrated PBL curriculum. Both the AI assistants generated excellent clinical vignettes and themes; however, most of the cluster MCQs by ChatGPT had “None of the above” as an option, which is often considered a test item construction flaw. Notwithstanding these limitations, case cluster integrated test items are valuable for learners to integrate their knowledge of different basic medical sciences and their application to clinical sciences. This integrated approach can be used for both instructional and student assessment purposes to make the course more meaningful. Indeed, one of the basic tenets of PBL is curriculum integration.

In the next query, we asked AI assistants to write integrated case cluster MCQs with 2 test items in each cluster with 5 options for undergraduate medical students at the time of graduation integrating pharmacology and physiology of hypertension with a case vignette. The responses are listed in the electronic supplementary materials 19 – 21 . Claude-Instant has provided only partial responses to both the queries. Overall, the information provided in case vignettes requires the learner to interpret the clinical information and make therapeutic decisions. Such test items are appropriate for assessing the minimum competency of medical school graduates to start internship or residency programs; these MCQs are generally comparable to test items used in USMLE Part 2. The evaluation of the integrated case cluster test items provided by all the AI platforms is summarized in Table  3 . In general, the AI outputs had at least one construction defect, and did not include all the core sub-topics to a sufficient degree.

When asked to simulate the above-generated test items to 100 undergraduate medical students at the time of graduation and provide a percentage of students who are likely to correctly answer each item, the AI assistant responded:

“ As an AI language model, I cannot simulate the responses of 100 undergraduate medical students likely to obtain the correct answer for each question. However, I can provide some general information about the difficulty level of each question based on the complexity of the concepts being tested and the level of integration between pharmacology and physiology ”.

AI assistant went on to clarify that questions that require knowledge of basic pharmacology principles, such as the mechanism of action of specific drugs, are likely to be easier for students to answer correctly. Test items that require an understanding of the physiological mechanisms underlying hypertension and correlating with symptoms are likely to be more challenging for students. The AI assistant sorted these test items into two categories accordingly. Overall, the difficulty level of the test item is based on the level of integration between pharmacology and pathophysiology. Test items that require an understanding of both pharmacological and physiological mechanisms are likely to be more challenging for students requiring a strong foundation in both pharmacology and physiology concepts to be able to correctly answer integrated case-cluster MCQs.

Short answer questions

The responses to a search query on generating SAQs appropriate to the pre-clerkship phase Sage Poe, Claude-Instant, and ChatGPT generated items are listed in the electronic supplementary materials 22 – 24 for difficult questions and 25–27 for moderately difficult questions.

It is apparent from these case vignette descriptions that the short answer question format varied. Accordingly, the scope for asking individual questions for each scenario is open-ended. In all instances, model answers are supplied which are helpful for the course instructor to plan classroom lessons, identify appropriate instructional methods, and establish rubrics for grading the answer scripts, and as a study guide for students.

We then wanted to see to what extent AI can differentiate the difficulty of the SAQ by replacing the search term “difficult” with “moderately difficult” in the above search prompt: the changes in the revised case scenarios are substantial. Perhaps the context of learning and practice (and the level of the student in the MD/medical program) may determine the difficulty level of SAQ generated. It is worth noting that on changing the search from cardiology to internal medicine rotation in Sage Poe the case description also changed. Thus, it is essential to select an appropriate AI assistant, perhaps by trial and error, to generate quality SAQs. Most of the individual questions tested stand-alone knowledge and did not require students to demonstrate integration.

The responses of Sage Poe, Claude-Instant, and ChatGPT for the search query to generate SAQs at the time of graduation are listed in the electronic supplementary materials 28 – 30 . It is interesting to note how AI assistants considered the stage of the learner while generating the SAQ. The response by Sage Poe is illustrative for comparison. “You are a newly graduated medical student who is working in a hospital” versus “You are a medical student in your pre-clerkship.”

Some questions were retained, deleted, or modified to align with competency appropriate to the context (Electronic Supplementary Materials 28 – 30 ). Overall, the test items at both levels from all AI platforms were technically accurate and thorough addressing the topics related to different disciplines (Table  3 ). The differences in learning objective transition are summarized in Table  4 . A comparison of learning objectives revealed that almost all objectives remained the same except for a few (Table  5 ).

A similar trend was apparent with test items generated by other AI assistants, such as ChatGPT. The contrasting differences in questions are illustrated by the vertical integration of basic sciences and clinical sciences (Table  6 ).

Taken together, these in-depth qualitative comparisons suggest that AI assistants such as Sage Poe and ChatGPT consider the learner’s stage of training in designing test items, learning outcomes, and answers expected from the examinee. It is critical to state the search query explicitly to generate quality output by AI assistants.

The OSPE test items generated by Claude-Instant and ChatGPT appropriate to the pre-clerkship phase (without mentioning “appropriate instructions for the patients”) are listed in the electronic supplementary materials 31 and 32 and with patient instructions on the electronic supplementary materials 33 and 34 . For reasons unknown, Sage Poe did not provide any response to this search query.

The five OSPE items generated were suitable to assess the prescription writing competency of pre-clerkship medical students. The clinical scenarios identified by the three AI platforms were comparable; these scenarios include patients with hypertension and impaired glucose tolerance in a 65-year-old male, hypertension with chronic kidney disease (CKD) in a 55-year-old woman, resistant hypertension with obstructive sleep apnea in a 45-year-old man, and gestational hypertension at 32 weeks in a 35-year-old (Claude-Instant AI). Incorporating appropriate instructions facilitates the learner’s ability to educate patients and maximize safe and effective therapy. The OSPE item required students to write a prescription with guidance to start conservatively, choose an appropriate antihypertensive drug class (drug) based on the patients’ profile, specifying drug name, dose, dosing frequency, drug quantity to be dispensed, patient name, date, refill, and caution as appropriate, in addition to prescribers’ name, signature, and license number. In contrast, ChatGPT identified clinical scenarios to include patients with hypertension and CKD, hypertension and bronchial asthma, gestational diabetes, hypertension and heart failure, and hypertension and gout (ChatGPT). Guidance for dosage titration, warnings to be aware, safety monitoring, and frequency of follow-up and dose adjustment. These test items are designed to assess learners’ knowledge of P & T of antihypertensives, as well as their ability to provide appropriate instructions to patients. These clinical scenarios for writing prescriptions assess students’ ability to choose an appropriate drug class, write prescriptions with proper labeling and dosing, reflect drug safety profiles, and risk factors, and make modifications to meet the requirements of special populations. The prescription is required to state the drug name, dose, dosing frequency, patient name, date, refills, and cautions or instructions as needed. A conservative starting dose, once or twice daily dosing frequency based on the drug, and instructions to titrate the dose slowly if required.

The responses from Claude-Instant and ChatGPT for the search query related to generating OSPE test items at the time of graduation are listed in electronic supplementary materials 35 and 36 . In contrast to the pre-clerkship phase, OSPEs generated for graduating doctors’ competence assessed more advanced drug therapy comprehension. For example, writing a prescription for:

(1) A 65-year- old male with resistant hypertension and CKD stage 3 to optimize antihypertensive regimen required the answer to include starting ACEI and diuretic, titrating the dosage over two weeks, considering adding spironolactone or substituting ACEI with an ARB, and need to closely monitor serum electrolytes and kidney function closely.

(2) A 55-year-old woman with hypertension and paroxysmal arrhythmia required the answer to include switching ACEI to ARB due to cough, adding a CCB or beta blocker for rate control needs, and adjusting the dosage slowly and monitoring for side effects.

(3) A 45-year-old man with masked hypertension and obstructive sleep apnea require adding a centrally acting antihypertensive at bedtime and increasing dosage as needed based on home blood pressure monitoring and refer to CPAP if not already using one.

(4) A 75-year-old woman with isolated systolic hypertension and autonomic dysfunction to require stopping diuretic and switching to an alpha blocker, upward dosage adjustment and combining with other antihypertensives as needed based on postural blood pressure changes and symptoms.

(5) A 35-year-old pregnant woman with preeclampsia at 29 weeks require doubling methyldopa dose and consider adding labetalol or nifedipine based on severity and educate on signs of worsening and to follow-up immediately for any concerning symptoms.

These case scenarios are designed to assess the ability of the learner to comprehend the complexity of antihypertensive regimens, make evidence-based regimen adjustments, prescribe multidrug combinations based on therapeutic response and tolerability, monitor complex patients for complications, and educate patients about warning signs and follow-up.

A similar output was provided by ChatGPT, with clinical scenarios such as prescribing for patients with hypertension and myocardial infarction; hypertension and chronic obstructive pulmonary airway disease (COPD); hypertension and a history of angina; hypertension and a history of stroke, and hypertension and advanced renal failure. In these cases, wherever appropriate, pharmacotherapeutic issues like taking ramipril after food to reduce side effects such as giddiness; selection of the most appropriate beta-blocker such as nebivolol in patients with COPD comorbidity; the importance of taking amlodipine at the same time every day with or without food; preference for telmisartan among other ARBs in stroke; choosing furosemide in patients with hypertension and edema and taking the medication with food to reduce the risk of gastrointestinal adverse effect are stressed.

The AI outputs on OSPE test times were observed to be technically accurate, thorough in addressing core sub-topics suitable for the learner’s level and did not have any construction defects (Table  3 ). Both AIs provided the model answers with explanatory notes. This facilitates the use of such OSPEs for self-assessment by learners for formative assessment purposes. The detailed instructions are helpful in creating optimized therapy regimens, and designing evidence-based regimens, to provide appropriate instructions to patients with complex medical histories. One can rely on multiple AI sources to identify, shortlist required case scenarios, and OSPE items, and seek guidance on expected model answers with explanations. The model answer guidance for antihypertensive drug classes is more appropriate (rather than a specific drug of a given class) from a teaching/learning perspective. We believe that these scenarios can be refined further by providing a focused case history along with relevant clinical and laboratory data to enhance clinical fidelity and bring a closer fit to the competency framework.

In the present study, AI tools have generated SLOs that comply with the current principles of medical education [ 15 ]. AI tools are valuable in constructing SLOs and so are especially useful for medical fraternities where training in medical education is perceived as inadequate, more so in the early stages of their academic career. Data suggests that only a third of academics in medical schools have formal training in medical education [ 16 ] which is a limitation. Thus, the credibility of alternatives, such as the AIs, is evaluated to generate appropriate course learning outcomes.

We observed that the AI platforms in the present study generated quality test items suitable for different types of assessment purposes. The AI-generated outputs were similar with minor variation. We have used generative AIs in the present study that could generate new content from their training dataset [ 17 ]. Problem-based and interactive learning approaches are referred to as “bottom-up” where learners obtain first-hand experience in solving the cases first and then indulge in discussion with the educators to refine their understanding and critical thinking skills [ 18 ]. We suggest that AI tools can be useful for this approach for imparting the core knowledge and skills related to Pharmacology and Therapeutics to undergraduate medical students. A recent scoping review evaluating the barriers to writing quality test items based on 13 studies has concluded that motivation, time constraints, and scheduling were the most common [ 19 ]. AI tools can be valuable considering the quick generation of quality test items and time management. However, as observed in the present study, the AI-generated test items nevertheless require scrutiny by faculty members for content validity. Moreover, it is important to train faculty in AI technology-assisted teaching and learning. The General Medical Council recommends taking every opportunity to raise the profile of teaching in medical schools [ 20 ]. Hence, both the academic faculty and the institution must consider investing resources in AI training to ensure appropriate use of the technology [ 21 ].

The AI outputs assessed in the present study had errors, particularly with A-type MCQs. One notable observation was that often the AI tools were unable to differentiate the differences between ACEIs and ARBs. AI platforms access several structured and unstructured data, in addition to images, audio, and videos. Hence, the AI platforms can commit errors due to extracting details from unauthenticated sources [ 22 ] created a framework identifying 28 factors for reconstructing the path of AI failures and for determining corrective actions. This is an area of interest for AI technical experts to explore. Also, this further iterates the need for human examination of test items before using them for assessment purposes.

There are concerns that AIs can memorize and provide answers from their training dataset, which they are not supposed to do [ 23 ]. Hence, the use of AIs-generated test items for summative examinations is debatable. It is essential to ensure and enhance the security features of AI tools to reduce or eliminate cross-contamination of test items. Researchers have emphasized that AI tools will only reach their potential if developers and users can access full-text non-PDF formats that help machines comprehend research papers and generate the output [ 24 ].

AI platforms may not always have access to all standard treatment guidelines. However, in the present study, it was observed that all three AI platforms generally provided appropriate test items regarding the choice of medications, aligning with recommendations from contemporary guidelines and standard textbooks in pharmacology and therapeutics. The prompts used in the study were specifically focused on the pre-clerkship phase of the undergraduate medical curriculum (and at the time of their graduation) and assessed fundamental core concepts, which were also reflected in the AI outputs. Additionally, the recommended first-line antihypertensive drug classes have been established for several decades, and information regarding their pharmacokinetics, ADRs, and indications is well-documented in the literature.

Different paradigms and learning theories have been proposed to support AI in education. These paradigms include AI- directed (learner as recipient), AI-supported (learner as collaborator), and AI-empowered (learner as leader) that are based on Behaviorism, Cognitive-Social constructivism, and Connectivism-Complex adaptive systems, respectively [ 25 ]. AI techniques have potential to stimulate and advance instructional and learning sciences. More recently a three- level model that synthesizes and unifies existing learning theories to model the roles of AIs in promoting learning process has been proposed [ 26 ]. The different components of our study rely upon these paradigms and learning theories as the theoretical underpinning.

Strengths and limitations

To the best of our knowledge, this is the first study evaluating the utility of AI platforms in generating test items related to a discipline in the undergraduate medical curriculum. We have evaluated the AI’s ability to generate outputs related to most types of assessment in the undergraduate medical curriculum. The key lessons learnt for improving the AI-generated test item quality from the present study are outlined in Table  7 . We used a structured framework for assessing the content validity of the test items. However, we have demonstrated using a single case study (hypertension) as a pilot experiment. We chose to evaluate anti-hypertensive drugs as it is a core learning objective and one of the most common disorders relevant to undergraduate medical curricula worldwide. It would be interesting to explore the output from AI platforms for other common (and uncommon/region-specific) disorders, non-/semi-core objectives, and disciplines other than Pharmacology and Therapeutics. An area of interest would be to look at the content validity of the test items generated for different curricula (such as problem-based, integrated, case-based, and competency-based) during different stages of the learning process. Also, we did not attempt to evaluate the generation of flowcharts, algorithms, or figures for generating test items. Another potential area for exploring the utility of AIs in medical education would be repeated procedural practices such as the administration of drugs through different routes by trainee residents [ 27 ]. Several AI tools have been identified for potential application in enhancing classroom instructions and assessment purposes pending validation in prospective studies [ 28 ]. Lastly, we did not administer the AI-generated test items to students and assessed their performance and so could not comment on the validity of test item discrimination and difficulty indices. Additionally, there is a need to confirm the generalizability of the findings to other complex areas in the same discipline as well as in other disciplines that pave way for future studies. The conceptual framework used in the present study for evaluating the AI-generated test items needs to be validated in a larger population. Future studies may also try to evaluate the variations in the AI outputs with repetition of the same queries.

Notwithstanding ongoing discussions and controversies, AI tools are potentially useful adjuncts to optimize instructional methods, test blueprinting, test item generation, and guidance for test standard-setting appropriate to learners’ stage in the medical program. However, experts need to critically review the content validity of AI-generated output. These challenges and caveats are to be addressed before the use of widespread use of AIs in medical education can be advocated.

Data availability

All the data included in this study are provided as Electronic Supplementary Materials.

Tolsgaard MG, Pusic MV, Sebok-Syer SS, Gin B, Svendsen MB, Syer MD, Brydges R, Cuddy MM, Boscardin CK. The fundamentals of Artificial Intelligence in medical education research: AMEE Guide 156. Med Teach. 2023;45(6):565–73.

Article   Google Scholar  

Sriwastwa A, Ravi P, Emmert A, Chokshi S, Kondor S, Dhal K, Patel P, Chepelev LL, Rybicki FJ, Gupta R. Generative AI for medical 3D printing: a comparison of ChatGPT outputs to reference standard education. 3D Print Med. 2023;9(1):21.

Azer SA, Guerrero APS. The challenges imposed by artificial intelligence: are we ready in medical education? BMC Med Educ. 2023;23(1):680.

Masters K. Ethical use of Artificial Intelligence in Health Professions Education: AMEE Guide 158. Med Teach. 2023;45(6):574–84.

Nagi F, Salih R, Alzubaidi M, Shah H, Alam T, Shah Z, Househ M. Applications of Artificial Intelligence (AI) in Medical Education: a scoping review. Stud Health Technol Inf. 2023;305:648–51.

Google Scholar  

Mehta N, Harish V, Bilimoria K, et al. Knowledge and attitudes on artificial intelligence in healthcare: a provincial survey study of medical students. MedEdPublish. 2021;10(1):75.

Mir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, Alharthi MH, Alamri MMS. Application of Artificial Intelligence in Medical Education: current scenario and future perspectives. J Adv Med Educ Prof. 2023;11(3):133–40.

Garg T. Artificial Intelligence in Medical Education. Am J Med. 2020;133(2):e68.

Matheny ME, Whicher D, Thadaney IS. Artificial intelligence in health care: a report from the National Academy of Medicine. JAMA. 2020;323(6):509–10.

Sage Poe. Available at: https://poe.com/Assistant (Accessed on. 3rd June 2023).

Claude-Instant: Available at: https://poe.com/Claude-instant (Accessed on 3rd. June 2023).

ChatGPT: Available at: https://poe.com/ChatGPT (Accessed on 3rd. June 2023).

James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, Lackland DT, LeFevre ML, MacKenzie TD, Ogedegbe O, Smith SC Jr, Svetkey LP, Taler SJ, Townsend RR, Wright JT Jr, Narva AS, Ortiz E. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014;311(5):507–20.

Eschenhagen T. Treatment of hypertension. In: Brunton LL, Knollmann BC, editors. Goodman & Gilman’s the pharmacological basis of therapeutics. 14th ed. New York: McGraw Hill; 2023.

Shabatura J. September. Using Bloom’s taxonomy to write effective learning outcomes. https://tips.uark.edu/using-blooms-taxonomy/ (Accessed on 19th 2023).

Trainor A, Richards JB. Training medical educators to teach: bridging the gap between perception and reality. Isr J Health Policy Res. 2021;10(1):75.

Boscardin C, Gin B, Golde PB, Hauer KE. ChatGPT and generative artificial intelligence for medical education: potential and opportunity. Acad Med. 2023. https://doi.org/10.1097/ACM.0000000000005439 . (Published ahead of print).

Duong MT, Rauschecker AM, Rudie JD, Chen PH, Cook TS, Bryan RN, Mohan S. Artificial intelligence for precision education in radiology. Br J Radiol. 2019;92(1103):20190389.

Karthikeyan S, O’Connor E, Hu W. Barriers and facilitators to writing quality items for medical school assessments - a scoping review. BMC Med Educ. 2019;19(1):123.

Developing teachers and trainers in undergraduate medical education. Advice supplementary to Tomorrow’s Doctors. (2009). https://www.gmc-uk.org/-/media/documents/Developing_teachers_and_trainers_in_undergraduate_medical_education___guidance_0815.pdf_56440721.pdf (Accessed on 19th September 2023).

Cooper A, Rodman A. AI and Medical Education - A 21st-Century Pandora’s Box. N Engl J Med. 2023;389(5):385–7.

Chanda SS, Banerjee DN. Omission and commission errors underlying AI failures. AI Soc. 2022;17:1–24.

Narayanan A, Kapoor S. ‘GPT-4 and Professional Benchmarks: The Wrong Answer to the Wrong Question’. Substack newsletter. AI Snake Oil (blog). https://aisnakeoil.substack.com/p/gpt-4-and-professional-benchmarks (Accessed on 19th September 2023).

Brainard J. November. As scientists face a flood of papers, AI developers aim to help. Science, 21 2023. doi.10.1126/science.adn0669.

Ouyang F, Jiao P. Artificial intelligence in education: the three paradigms. Computers Education: Artif Intell. 2021;2:100020.

Gibson D, Kovanovic V, Ifenthaler D, Dexter S, Feng S. Learning theories for artificial intelligence promoting learning processes. Br J Edu Technol. 2023;54(5):1125–46.

Guerrero DT, Asaad M, Rajesh A, Hassan A, Butler CE. Advancing Surgical Education: the Use of Artificial Intelligence in Surgical Training. Am Surg. 2023;89(1):49–54.

Lee S. AI tools for educators. EIT InnoEnergy Master School Teachers Conference. 2023. https://www.slideshare.net/ignatia/ai-toolkit-for-educators?from_action=save (Accessed on 24th September 2023).

Download references

Author information

Authors and affiliations.

Department of Pharmacology & Therapeutics, College of Medicine & Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain

Kannan Sridharan & Reginald P. Sequeira

You can also search for this author in PubMed   Google Scholar

Contributions

RPS– Conceived the idea; KS– Data collection and curation; RPS and KS– Data analysis; RPS and KS– wrote the first draft and were involved in all the revisions.

Corresponding author

Correspondence to Kannan Sridharan .

Ethics declarations

Ethics approval and consent to participate.

Not applicable as neither there was any interaction with humans, nor any personal data was collected in this research study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Sridharan, K., Sequeira, R.P. Artificial intelligence and medical education: application in classroom instruction and student assessment using a pharmacology & therapeutics case study. BMC Med Educ 24 , 431 (2024). https://doi.org/10.1186/s12909-024-05365-7

Download citation

Received : 26 September 2023

Accepted : 28 March 2024

Published : 22 April 2024

DOI : https://doi.org/10.1186/s12909-024-05365-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Medical education
• Pharmacology
• Therapeutics

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Heart-Healthy Living
• High Blood Pressure
• Sickle Cell Disease
• Sleep Apnea
• Information & Resources on COVID-19
• The Heart Truth®
• Learn More Breathe Better®
• Blood Diseases and Disorders Education Program
• Publications and Resources
• Blood Disorders and Blood Safety
• Sleep Science and Sleep Disorders
• Lung Diseases
• Health Disparities and Inequities
• Heart and Vascular Diseases
• Precision Medicine Activities
• Obesity, Nutrition, and Physical Activity
• Population and Epidemiology Studies
• Women’s Health
• Research Topics
• Clinical Trials
• All Science A-Z
• Grants and Training Home
• Policies and Guidelines
• Funding Opportunities and Contacts
• Training and Career Development
• Email Alerts
• NHLBI in the Press
• Research Features
• Past Events
• Upcoming Events
• Mission and Strategic Vision
• Divisions, Offices and Centers
• Advisory Committees
• Budget and Legislative Information
• Jobs and Working at the NHLBI
• Contact and FAQs
• NIH Sleep Research Plan
• News and Events
• < Back To All News

Protecting Navajo children with asthma: A case study

Joncita Todechine, a mother of four who lives on the Navajo Nation, knows all too well what can trigger asthma symptoms in her daughter Ashley. But she didn’t always. She recalls a time in 2013, living in Phoenix and attending medical assistant school, when she rushed her then-three-year-old to the Indian Medical Center. 

“She was really sick,” Todechine said. “She was fevering, coughing, and had shortness of breath. We had no idea what was wrong.”

Ashley was admitted to the hospital and stayed for an entire week before the doctors could make a diagnosis of asthma. Now a thriving 13-year-old, Ashley loves gaming, social media, and riding on her hoverboard. These days she lives on the Navajo reservation with her family, who moved there shortly after her mother finished school. For the most part, she keeps her asthma under control by taking medication and doing her best to avoid her asthma triggers. 

But that can be challenging.

On the Navajo Nation, there are many asthma triggers. The semi-arid environment is plagued by drought, so on windy days, the gusts kick up ever-present dust and sand into the air. Shuttered coal-fired powerplants dot the landscape and, though they are closed, residual soot still dirties the air. Uranium and other heavy metals contaminate the landscape, and people breathe diesel fumes from the buses that take children to and from school every day. The many dogs and livestock roaming the reservation carry other allergens.

“And that’s just the outdoor pollution,” said Bruce Bender, Ph.D., professor in the pediatrics department at National Jewish Health in Denver, Colorado. “Seventy percent of households heat with indoor stoves that burn wood or charcoal and can leak a lot of smoke into the air.”

Bender would know. He’s co-project leader of an NHLBI-funded project focused on reducing health disparities in children living on the Navajo Nation, and he’s studied some of the factors that make those disparities worse. He’s also looked at the health data overall and found that while Native adults suffer from higher rates of chronic conditions like cardiovascular diseases and diabetes, it’s asthma that remains one of the most common chronic diseases in children. Some 18% of children on the Navajo reservation have it,  compared to 10.2% of children nationwide.

“Asthma can be incredibly scary for children and their families, especially those who cannot get emergency care easily or quickly,” said Michelle Freemer, M.D., M.P.H., director of the asthma program in NHLBI’s Division of Lung Diseases.

The Navajo Nation extends across more than 27,000 square miles, making it the largest Native land area in the U.S. "For families of children with asthma, the distances and travel conditions on the reservation may add challenges,” said Freemer. “The investigators partnered with the community to find solutions that work where they live, not simply provide asthma care that has been shown to work in other places." 

A local solution

Bender and his colleague, Lynn B. Gerald, Ph.D., M.S.P.H., assistant vice chancellor for population health at the University of Illinois-Chicago, started a large-scale effort to teach educators, children, their families, and local medical providers on the Navajo Nation how to identify an asthma attack and what to do in an emergency. (Gerald had worked previously at the University of Arizona and had gained a wealth of knowledge from the university's Native collaborators.) The program rolled out in three Arizona communities on the reservation: Tuba City, Chinle, and Fort Defiance. Combined, these towns represent 43% of the Navajo Nation population and are home to more than 8,000 children with asthma.  

But before they began, the investigators knew they needed to build relationships with the Navajo people – who refer to themselves as Diné – as the community’s prior experiences with non-Native researchers had left them skeptical. The research team began by ensuring the program was tailored to the needs and wishes of the community itself.

“The Navajo Nation human research review board is very careful and thorough,” Bender said. “They’re protecting their population. We had to earn their trust.”

Once the investigators got approval, they hit the ground running, starting in Tuba City. In the hospitals, the research team provided tools for medical professionals, using self-directed online learning and in-person workshops, to increase their use of practices that have been shown to be important in asthma care.

In the schools, the investigators provided education using the American Lung Association’s Asthma Basics and Open Airways for Schools® training, to teach school staff about asthma, its triggers, and what to do when a child is having an attack and to teach Diné children how to manage their asthma. 

Using a “train-the-trainer” model, school staff, community health workers, respiratory therapists, and pharmacists became students and then instructors. This made it possible for the Diné participants to teach additional staff, ensuring the community can sustain the program after the research funding ends.

Still, there was another urgent need that Bender and Gerald realized had not been addressed. “Less than 15% of children with asthma actually have an inhaler at school when they need it,” Gerald said. In response, the team helped start a program in two of the three communities that provided stock inhalers to schools for children who need them. 

A global threat

After starting the program in Tuba City as planned and spending a year there, the research team moved their focus to Chinle. The goal was to be able to compare how well the program worked in each of the three communities. But a global pandemic had other plans.

“The COVID-19 pandemic hit right in the middle of our time in Chinle,” Bender said. “After that, we weren’t allowed on the reservation for two years.”

While the pandemic changed life for all Americans, it devastated many Native communities. Schools closed and medical clinics focused on emergencies. The research team pivoted: they continued some training virtually and were able to keep learning from families about their needs, especially using the Diné members of the research team who were on the reservation.

Taking stock

Today, despite the challenges of the pandemic, all three communities have completed the original program, and 439 Diné members have been trained to identify asthma and its triggers. Yet the work is far from over. The investigators are analyzing the data they collected. “Particularly important is returning the results to the community,” Gerald said. As soon as they are ready, she said, they will be meeting with the school boards and health boards and joining community meetings to share them.

Freemer said that all the materials the researchers developed through their NHLBI funding are available to the community and have also been shared with those at the Indian Health Service leading the Asthma Control in Tribal Communities program.

“The researchers also took the opportunity to build research capacity,” she said. They developed an agreement with Diné College, the only four-year college on the reservation, to provide training through their Summer Research Experience Program. “In that program, students learned about research and were able to readily reach the families who appreciated the interactions with Diné research team members.”

Todechine said knowing that her child will be cared for if the worst happens has given her peace of mind. “Now the school systems have their own asthma alert systems that the employees and even the bus drivers take part in,” she said. “For me, I feel safer for her to be at school without me.”

  Resources:

Learn more about  Asthma in Our Communities  with specific resources for American Indians.

Related Health Topics

Loading metrics

Open Access

Peer-reviewed

Research Article

Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] (AJT); [email protected] (DSJT)

Affiliations University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom, Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, United Kingdom

Roles Data curation, Investigation, Writing – review & editing

Affiliation University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom

Affiliation Eye Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates

Roles Data curation, Investigation, Writing – original draft, Writing – review & editing

Affiliations University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom

Roles Data curation, Investigation

Affiliation West Suffolk NHS Foundation Trust, Bury St Edmunds, United Kingdom

Affiliation Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom

Affiliation Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Foundation Trust, Birmingham, United Kingdom

Affiliation Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan

Affiliation Yong Loo Lin School of Medicine, National University of Singapore, Singapore

Roles Data curation, Investigation, Project administration, Writing – review & editing

Affiliation Bedfordshire Hospitals NHS Foundation Trust, Luton and Dunstable, United Kingdom

Affiliation Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

Roles Writing – review & editing

Affiliations Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Foundation Trust, Birmingham, United Kingdom, Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom

Roles Funding acquisition, Project administration

Affiliations Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore, Duke-NUS Medical School, Singapore, Singapore, Byers Eye Institute, Stanford University, Palo Alto, California, United States of America

•  [ ... ],

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliations Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Foundation Trust, Birmingham, United Kingdom, Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom, Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom

• [ view all ]
• [ view less ]
• Arun James Thirunavukarasu, 
• Shathar Mahmood, 
• Andrew Malem, 
• William Paul Foster, 
• Rohan Sanghera, 
• Refaat Hassan, 
• Sean Zhou, 
• Shiao Wei Wong, 
• Yee Ling Wong, 

• Published: April 17, 2024
• https://doi.org/10.1371/journal.pdig.0000341
• Reader Comments

Large language models (LLMs) underlie remarkable recent advanced in natural language processing, and they are beginning to be applied in clinical contexts. We aimed to evaluate the clinical potential of state-of-the-art LLMs in ophthalmology using a more robust benchmark than raw examination scores. We trialled GPT-3.5 and GPT-4 on 347 ophthalmology questions before GPT-3.5, GPT-4, PaLM 2, LLaMA, expert ophthalmologists, and doctors in training were trialled on a mock examination of 87 questions. Performance was analysed with respect to question subject and type (first order recall and higher order reasoning). Masked ophthalmologists graded the accuracy, relevance, and overall preference of GPT-3.5 and GPT-4 responses to the same questions. The performance of GPT-4 (69%) was superior to GPT-3.5 (48%), LLaMA (32%), and PaLM 2 (56%). GPT-4 compared favourably with expert ophthalmologists (median 76%, range 64–90%), ophthalmology trainees (median 59%, range 57–63%), and unspecialised junior doctors (median 43%, range 41–44%). Low agreement between LLMs and doctors reflected idiosyncratic differences in knowledge and reasoning with overall consistency across subjects and types ( p >0.05). All ophthalmologists preferred GPT-4 responses over GPT-3.5 and rated the accuracy and relevance of GPT-4 as higher ( p <0.05). LLMs are approaching expert-level knowledge and reasoning skills in ophthalmology. In view of the comparable or superior performance to trainee-grade ophthalmologists and unspecialised junior doctors, state-of-the-art LLMs such as GPT-4 may provide useful medical advice and assistance where access to expert ophthalmologists is limited. Clinical benchmarks provide useful assays of LLM capabilities in healthcare before clinical trials can be designed and conducted.

Author summary

Large language models (LLMs) are the most sophisticated form of language-based artificial intelligence. LLMs have the potential to improve healthcare, and experiments and trials are ongoing to explore potential avenues for LLMs to improve patient care. Here, we test state-of-the-art LLMs on challenging questions used to assess the aptitude of eye doctors (ophthalmologists) in the United Kingdom before they can be deemed fully qualified. We compare the performance of these LLMs to fully trained ophthalmologists as well as doctors in training to gauge the aptitude of the LLMs for providing advice to patients about eye health. One of the LLMs, GPT-4, exhibits favourable performance when compared with fully qualified and training ophthalmologists; and comparisons with its predecessor model, GPT-3.5, indicate that this superior performance is due to improved accuracy and relevance of model responses. LLMs are approaching expert-level ophthalmological knowledge and reasoning, and may be useful for providing eye-related advice where access to healthcare professionals is limited. Further research is required to explore potential avenues of clinical deployment.

Citation: Thirunavukarasu AJ, Mahmood S, Malem A, Foster WP, Sanghera R, Hassan R, et al. (2024) Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study. PLOS Digit Health 3(4): e0000341. https://doi.org/10.1371/journal.pdig.0000341

Editor: Man Luo, Mayo Clinic Scottsdale, UNITED STATES

Received: July 31, 2023; Accepted: February 26, 2024; Published: April 17, 2024

Copyright: © 2024 Thirunavukarasu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are available as supplementary information , excluding copyrighted material from the textbook used for experiments.

Funding: DSWT is supported by the National Medical Research Council, Singapore (NMCR/HSRG/0087/2018; MOH-000655-00; MOH-001014-00), Duke-NUS Medical School (Duke-NUS/RSF/2021/0018; 05/FY2020/EX/15-A58), and Agency for Science, Technology and Research (A20H4g2141; H20C6a0032). DSJT is supported by a Medical Research Council / Fight for Sight Clinical Research Fellowship (MR/T001674/1). These funders were not involved in the conception, execution, or reporting of this review.

Competing interests: AM is a member of the Panel of Examiners of the Royal College of Ophthalmologists and performs unpaid work as an FRCOphth examiner. DSWT holds a patent on a deep learning system to detect retinal disease. DSJT authored the book used in the study and receives royalty from its sales. The other authors have no competing interests to declare.

Introduction

Generative Pre-trained Transformer 3.5 (GPT-3.5) and 4 (GPT-4) are large language models (LLMs) trained on datasets containing hundreds of billions of words from articles, books, and other internet sources [ 1 , 2 ]. ChatGPT is an online chatbot which uses GPT-3.5 or GPT-4 to provide bespoke responses to human users’ queries [ 3 ]. LLMs have revolutionised the field of natural language processing, and ChatGPT has attracted significant attention in medicine for attaining passing level performance in medical school examinations and providing more accurate and empathetic messages than human doctors in response to patient queries on a social media platform [ 3 , 4 , 5 , 6 ]. While GPT-3.5 performance in more specialised examinations has been inadequate, GPT-4 is thought to represent a significant advancement in terms of medical knowledge and reasoning [ 3 , 7 , 8 ]. Other LLMs in wide use include Pathways Language Model 2 (PaLM 2) and Large Language Model Meta AI 2 (LLaMA 2) [ 3 ], [ 9 , p. 2], [ 10 ].

Applications and trials of LLMs in ophthalmological settings has been limited despite ChatGPT’s performance in questions relating to ‘eyes and vision’ being superior to other subjects in an examination for general practitioners [ 7 , 11 ]. ChatGPT has been trialled on the North American Ophthalmology Knowledge Assessment Program (OKAP), and Fellowship of the Royal College of Ophthalmologists (FRCOphth) Part 1 and Part 2 examinations. In both cases, relatively poor results have been reported for GPT-3.5, with significant improvement exhibited by GPT-4 [ 12 , 13 , 14 , 15 , 16 ]. However, previous studies are afflicted by two important issues which may affect their validity and interpretability. First, so-called ‘contamination’, where test material features in the pretraining data used to develop LLMs, may result in inflated performance as models recall previously seen text rather than using clinical reasoning to provide an answer. Second, examination performance in and of itself provides little information regarding the potential of models to contribute to clinical practice as a medical-assistance tool [ 3 ]. Clinical benchmarks are required to understanding the meaning and implications of scores in ophthalmological examinations attained by LLMs and are a necessary precursor to clinical trials of LLM-based interventions.

Here, we used FRCOphth Part 2 examination questions to gauge the ophthalmological knowledge base and reasoning capability of LLMs using fully qualified and currently training ophthalmologists as clinical benchmarks. These questions were not freely available online, minimising the risk of contamination. The FRCOphth Part 2 Written Examination tests the clinical knowledge and skills of ophthalmologists in training using multiple choice questions with no negative marking and must be passed to fully qualify as a specialist eye doctor in the United Kingdom.

Question extraction

FRCOphth Part 2 questions were sourced from a textbook for doctors preparing to take the examination [ 17 ]. This textbook is not freely available on the internet, making the possibility of its content being included in LLMs’ training datasets unlikely [ 1 ]. All 360 multiple-choice questions from the textbook’s six chapters were extracted, and a 90-question mock examination from the textbook was segregated for LLM and doctor comparisons. Two researchers matched the subject categories of the practice papers’ questions to those defined in the Royal College of Ophthalmologists’ documentation concerning the FRCOphth Part 2 written examination. Similarly, two researchers categorised each question as first order recall or higher order reasoning, corresponding to ‘remembering’ and ‘applying’ or ‘analysing’ in Bloom’s taxonomy, respectively [ 18 ]. Disagreement between classification decisions was resolved by a third researcher casting a deciding vote. Questions containing non-plain text elements such as images were excluded as these could not be inputted to the LLM applications.

Trialling large language models

Every eligible question was inputted into ChatGPT (GPT-3.5 and GPT-4 versions; OpenAI, San Francisco, California, United States of America) between April 29 and May 10, 2023. The answers provided by GPT-3.5 and GPT-4 were recorded and their whole reply to each question was recorded for further analysis. If ChatGPT failed to provide a definitive answer, the question was re-trialled up to three times, after which ChatGPT’s answer was recorded as ‘null’ if no answer was provided. Correct answers (‘ground truth’) were defined as the answers provided by the textbook and were recorded for every eligible question to facilitate calculation of performance. Upon their release, Bard (Google LLC, Mountain View, California, USA) and HuggingChat (Hugging Face, Inc., New York City, USA) were used to trial PaLM 2 (Google LLC) and LLaMA (Meta, Menlo Park, California, USA) respectively on the portion of the textbook corresponding to a 90-question examination, adhering to the same procedures between June 20 and July 2, 2023.

Clinical benchmarks

To gauge the performance, accuracy, and relevance of LLM outputs, five expert ophthalmologists who had all passed the FRCOphth Part 2 (E1-E5), three trainees (residents) currently in ophthalmology training programmes (T1-T3), and two unspecialised ( i . e . not in ophthalmology training) junior doctors (J1-J2) first answered the 90-question mock examination independently, without reference to textbooks, the internet, or LLMs’ recorded answers. As with the LLMs, doctors’ performance was calculated with reference to the correct answers provided by the textbook. After completing the examination, ophthalmologists graded the whole output of GPT-3.5 and GPT-4 on a Likert scale from 1–5 (very bad, bad, neutral, good, very good) to qualitatively appraise accuracy of information provided and relevance of outputs to the question used as an input prompt. For these appraisals, ophthalmologists were blind to the LLM source (which was presented in a randomised order) and to their previous answers to the same questions, but they could refer to the question text and correct answer and explanation provided by the textbook. Procedures are comprehensively described in the protocol issued to the ophthalmologists ( S1 Protocol ).

Our null hypothesis was that LLMs and doctors would exhibit similar performance, supported by results in a wide range of medical examinations [ 3 , 6 ]. Prospective power analysis was conducted which indicated that 63 questions were required to identify a 10% superior performance of an LLM to human performance at a 5% significance level (type 1 error rate) with 80% power (20% type 2 error rate). This indicated that the 90-question examination in our experiments was more than sufficient to detect ~10% differences in overall performance. The whole 90-question mock examination was used to avoid over- or under-sampling certain question types with respect to actual FRCOphth papers. To verify that the mock examination was representative of the FRCOphth Part 2 examination, expert ophthalmologists were asked to rate the difficulty of questions used here in comparison to official examinations on a 5-point Likert scale (“much easier”, “somewhat easier”, “similar”, “somewhat more difficult”, “much more difficult”).

Statistical analysis

Performance of doctors and LLMs were compared using chi-squared (χ 2 ) tests. Agreement between answers provided by doctors and LLMs was quantified through calculation of Kappa statistics, interpreted in accordance with McHugh’s recommendations [ 19 ]. To further explore the strengths and weaknesses of the answer providers, performance was stratified by question type (first order fact recall or higher order reasoning) and subject using a chi-squared or Fisher’s exact test where appropriate. Likert scale data corresponding to the accuracy and relevance of GPT-3.5 and GPT-4 responses to the same questions were analysed with paired t -tests with the Bonferroni correction applied to mitigate the risk of false positive results due to multiple-testing—parametric testing was justified by a sufficient sample size [ 20 ]. A chi-squared test was used to quantify the significance of any difference in overall preference of ophthalmologists choosing between GPT-3.5 and GPT-4 responses. Statistical significance was concluded where p < 0.05. For additional contextualisation, examination statistics corresponding to FRCOphth Part 2 written examinations taken between July 2017 and December 2022 were collected from Royal College of Ophthalmologists examiners’ reports [ 21 ]. These statistics facilitated comparisons between human and LLM performance in the mock examination with the performance of actual candidates in recent examinations. Failure cases where all LLMs provided an incorrect answer were appraised qualitatively to explore any specific weaknesses of the technology.

Statistical analysis was conducted in R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria), and figures were produced in Affinity Designer (version 1.10.6; Serif Ltd, West Bridgford, Nottinghamshire, United Kingdom).

Questions sources

Of 360 questions in the textbook, 347 questions (including 87 of the 90 questions from the mock examination chapter) were included [ 17 ]. Exclusions were all due to non-text elements such as images and tables which could not be inputted into LLM chatbot interfaces. The distribution of question types and subjects within the whole set and mock examination set of questions is summarised in Table 1 and S1 Table alongside performance.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Question subject and type distributions presented alongside scores attained by LLMs (GPT-3.5, GPT-4, LLaMA, and PaLM 2), expert ophthalmologists (E1-E5), ophthalmology trainees (T1-T3), and unspecialised junior doctors (J1-J2). Median scores do not necessarily sum to the overall median score, as fractional scores are impossible.

https://doi.org/10.1371/journal.pdig.0000341.t001

GPT-4 represents a significant advance on GPT-3.5 in ophthalmological knowledge and reasoning.

Overall performance over 347 questions was significantly higher for GPT-4 (61.7%) than GPT-3.5 (48.41%; χ 2 = 12.32, p <0.01), with results detailed in S1 Fig and S1 Table . ChatGPT performance was consistent across question types and subjects ( S1 Table ). For GPT-4, no significant variation was observed with respect to first order and higher order questions (χ 2 = 0.22, p = 0.64), or subjects defined by the Royal College of Ophthalmologists (Fisher’s exact test over 2000 iterations, p = 0.23). Similar results were observed for GPT-3.5 with respect to first and second order questions (χ 2 = 0.08, p = 0.77), and subjects (Fisher’s exact test over 2000 iterations, p = 0.28). Performance and variation within the 87-question mock examination was very similar to the overall performance over 347 questions, and subsequent experiments were therefore restricted to that representative set of questions.

GPT-4 compares well with other LLMs, junior and trainee doctors and ophthalmology experts.

Performance in the mock examination is summarised in Fig 1 —GPT-4 (69%) was the top-scoring model, performing to a significantly higher standard than GPT-3.5 (48%; χ 2 = 7.33, p < 0.01) and LLaMA (32%; χ 2 = 22.77, p < 0.01), but statistically similarly to PaLM 2 (56%) despite a superior score (χ 2 = 2.81, p = 0.09). LLaMA exhibited the lowest examination score, significantly weaker than GPT-3.5 (χ 2 = 4.58, p = 0.03) and PaLM-2 (χ 2 = 10.01, p < 0.01) as well as GPT-4.

Examination performance in the 87-question mock examination used to trial LLMs (GPT-3.5, GPT-4, LLaMA, and PaLM 2), expert ophthalmologists (E1-E5), ophthalmology trainees (T1-T3), and unspecialised junior doctors (J1-J2). Dotted lines depict the mean performance of expert ophthalmologists (66/87; 76%), ophthalmology trainees (60/87; 69%), and unspecialised junior doctors (37/87; 43%). The performance of GPT-4 lay within the range of expert ophthalmologists and ophthalmology trainees.

https://doi.org/10.1371/journal.pdig.0000341.g001

The performance of GPT-4 was statistically similar to the mean score attained by expert ophthalmologists ( Fig 1 ; χ 2 = 1.18, p = 0.28). Moreover, GPT-4’s performance exceeded the mean mark attained across FRCOphth Part 2 written examination candidates between 2017–2022 (66.06%), mean pass mark according to standard setting (61.31%), and the mean official mark required to pass the examination after adjustment (63.75%), as detailed in S2 Table . In individual comparisons with expert ophthalmologists, GPT-4 was equivalent in 3 cases (χ 2 tests, p > 0.05, S3 Table ), and inferior in 2 cases (χ 2 tests, p < 0.05; Table 2 ). In comparisons with ophthalmology trainees, GPT-4 was equivalent to all three ophthalmology trainees (χ 2 tests, p > 0.05; Table 2 ). GPT-4 was significantly superior to both unspecialised trainee doctors (χ 2 tests, p < 0.05; Table 2 ). Doctors were anonymised in analysis, but their ophthalmological experience is summarised in S3 Table . Unsurprisingly, junior doctors (J1-J2) attained lower scores than expert ophthalmologists (E1-E5; t = 7.18, p < 0.01), and ophthalmology trainees (T1-T3; t = 11.18, p < 0.01), illustrated in Fig 1 . Ophthalmology trainees approached expert-level scores with no significant difference between the groups ( t = 1.55, p = 0.18). None of the other LLMs matched any of the expert ophthalmologists, mean mark of real examination candidates, or FRCOphth Part 2 pass mark.

Expert ophthalmologists agreed that the mock examination was a faithful representation of actual FRCOphth Part 2 Written Examination papers with a mean and median score of 3/5 (range 2-4/5).

Results of pair-wise comparisons of examination performance between GPT-4 and the other answer providers. Significantly greater performance for GPT-4 is highlighted green, significantly inferior performance for GPT-4 is highlighted orange. GPT-4 was superior to all other LLMs and unspecialised junior doctors, and equivalent to most expert ophthalmologists and all ophthalmology trainees.

https://doi.org/10.1371/journal.pdig.0000341.t002

LLM strengths and weaknesses are similar to doctors.

Agreement between answers given by LLMs, expert ophthalmologists, and trainee doctors was generally absent (0 ≤ κ < 0.2), minimal (0.2 ≤ κ < 0.4), or weak (0.4 ≤ κ < 0.6), with moderate agreement only recorded for one pairing between the two highest performing ophthalmologists ( Fig 2 ; κ = 0.64) [ 19 ]. Disagreement was primarily the result of general differences in knowledge and reasoning ability, illustrated by strong negative correlation between Kappa statistic (quantifying agreement) and difference in examination performance (Pearson’s r = -0.63, p < 0.01). Answer providers with more similar scores exhibited greater agreement overall irrespective of their category (LLM, expert ophthalmologist, ophthalmology trainee, or junior doctor).

Agreement correlates strongly with overall performance and stratification analysis found no particular question type or subject was associated with better performance of LLMs or doctors, indicating that LLM knowledge and reasoning ability is general across ophthalmology rather than restricted to particular subspecialties or question types.

https://doi.org/10.1371/journal.pdig.0000341.g002

Stratification analysis was undertaken to identify any specific strengths and weaknesses of LLMs with respect to expert ophthalmologists and trainee doctors ( Table 1 and S4 Table ). No significant difference between performance in first order fact recall and higher order reasoning questions was observed among any of the LLMs, expert ophthalmologists, ophthalmology trainees, or unspecialised junior doctors ( S4 Table ; χ 2 tests, p > 0.05). Similarly, only J1 (junior doctor yet to commence ophthalmology training) exhibited statistically significant variation in performance between subjects ( S4 Table ; Fisher’s exact tests over 2000 iterations, p = 0.02); all other doctors and LLMs exhibited no significant variation (Fisher’s exact tests over 2000 iterations, p > 0.05). To explore whether consistency was due to an insufficient sample size, similar analyses were run for GPT-3.5 and GPT-4 performance over the larger set of 347 questions ( S1 Table ; S4 Table ). As with the mock examination, no significant differences in performance across question types ( S4 Table ; χ 2 tests, p > 0.05) or subjects ( S4 Table ; Fisher’s exact tests over 2000 iterations, p > 0.05) were observed.

LLM examination performance translates to subjective preference indicated by expert ophthalmologists.

Ophthalmologists’ appraisal of GPT-4 and GPT-3.5 outputs indicated a marked preference for the former over the latter, mirroring objective performance in the mock examination and over the whole textbook. GPT-4 exhibited significantly ( t -test with Bonferroni correction, p < 0.05) higher accuracy and relevance than GPT-3.5 according to all five ophthalmologists’ grading ( Table 3 ). Differences were visually obvious, with GPT-4 exhibiting much higher rates of attaining the highest scores for accuracy and relevance than GPT-3.5 ( Fig 3 ). This superiority was reflected in ophthalmologists’ qualitative preference indications: GPT-4 responses were preferred to GPT-3.5 responses by every ophthalmologist with statistically significant skew in favour of GPT-4 (χ 2 test, p < 0.05; Table 3 ).

Accuracy (A) and relevance (B) ratings were provided by five expert ophthalmologists for ChatGPT (powered by GPT-3.5 and GPT-4) responses to 87 FRCOphth Part 2 mock examination questions. In every case, the accuracy and relevance of GPT-4 is significantly superior to GPT-3.5 (t-test with Bonferroni correct applied, p < 0.05). Pooled scores for accuracy (C) and relevance (D) from all five raters are presented in the bottom two plots, with GPT-3.5 (left bars) compared directly with GPT-4 (right bars).

https://doi.org/10.1371/journal.pdig.0000341.g003

t-test results with Bonferroni correction applied showing the superior accuracy and relevance of GPT-4 responses relative to GPT-3.5 responses in the opinion of five fully trained ophthalmologists (positive mean differences favour GPT-4), and χ 2 test showing that GPT-4 responses were preferred to GPT-3.5 responses by every ophthalmologist in their blinded qualitative appraisals.

https://doi.org/10.1371/journal.pdig.0000341.t003

Failure cases exhibit no association with subject, complexity, or human answers.

The LLM failure cases—where every LLM provided an incorrect answer—are summarised in Table 4 . While errors made by LLMs were occasionally similar to those made by trainee ophthalmologists and junior doctors, this association was not consistent ( Table 4 ). There was no preponderance of ophthalmological subject or first or higher order questions in the failure cases, and questions did not share a common theme, sentence structure, or grammatical construct ( Table 4 ). Examination questions are redacted here to avoid breaching copyright and prevent future LLMs accessing the test data during pretraining but can be provided on request.

Summary of LLM failure cases, where all models provided an incorrect answer to the FRCOphth Part 2 mock examination question. No associations were found with human answers, complexity, subject, theme, sentence structure, or grammatic constructs.

https://doi.org/10.1371/journal.pdig.0000341.t004

Here, we present a clinical benchmark to gauge the ophthalmological performance of LLMs, using a source of questions with very low risk of contamination as the utilised textbook is not freely available online [ 17 ]. Previous studies have suggested that ChatGPT can provide useful responses to ophthalmological queries, but often use online question sources which may have featured in LLMs’ pretraining datasets [ 7 , 12 , 15 , 22 ]. In addition, our employment of multiple LLMs as well as fully qualified and training doctors provides novel insight into the potential and limitations of state-of-the-art LLMs through head-to-head comparisons which provide clinical context and quantitative benchmarks of competence in ophthalmology. Subsequent research may leverage our questions and results to gauge the performance of new LLMs and applications as they emerge.

We make three primary observations. First, performance of GPT-4 compares well to expert ophthalmologists and ophthalmology trainees, and exhibits pass-worthy performance in an FRCOphth Part 2 mock examination. PaLM 2 did not attain pass-worthy performance or match expert ophthalmologists’ scores but was within the spread of trainee doctors’ performance. LLMs are approaching human expert-level knowledge and reasoning in ophthalmology, and significantly exceed the ability of non-specialist clinicians (represented here by unspecialised junior doctors) to answer ophthalmology questions. Second, clinician grading of model outputs suggests that GPT-4 exhibits improved accuracy and relevance when compared with GPT-3.5. Development is producing models which generate better outputs to ophthalmological queries in the opinion of expert human clinicians, which suggests that models are becoming more capable of providing useful assistance in clinical settings. Third, LLM performance was consistent across question subjects and types, distributed similarly to human performance, and exhibited comparable agreement between other LLMs and doctors when corrected for differences in overall performance. Together, this indicates that the ophthalmological knowledge and reasoning capability of LLMs is general rather than limited to certain subspecialties or tasks. LLM-driven natural language processing seems to facilitate similar—although idiosyncratic—clinical knowledge and reasoning to human clinicians, with no obvious blind spots precluding clinical use.

Similarly dramatic improvements in the performance of GPT-4 relative to GPT-3.5 have been reported in the context of the North American Ophthalmology Knowledge Assessment Program (OKAP) [ 13 , 15 ]. State-of-the-art models exhibit far more clinical promise than their predecessors, and expectations and development should be tailored accordingly. Results from the OKAP also suggest that improvement in performance is due to GPT-4 being more well-rounded than GPT-3.5 [ 13 ]. This increases the scope for potential applications of LLMs in ophthalmology, as development is eliminating weaknesses rather than optimising in narrow domains. This study shows that well-rounded LLM performance compares well with expert ophthalmologists, providing clinically relevant evidence that LLMs may be used to provide medical advice and assistance. Further improvement is expected as multimodal foundation models, perhaps based on LLMs such as GPT-4, emerge and facilitate compatibility with image-rich ophthalmological data [ 3 , 23 , 24 ].

Limitations

This study was limited by three factors. First, examination performance is an unvalidated indicator of clinical aptitude. We sought to ameliorate this limitation by employing expert ophthalmologists, ophthalmology trainees, and unspecialised junior doctors answering the same questions as clinical benchmarks; and compared LLM performance to real cohorts of candidates in recent FRCOphth examinations. However, it remains an issue that comparable performance to clinical experts in an examination does not necessarily demonstrate that an LLM can communicate with patients and practitioners or contribute to clinical decision making accurately and safely. Early trials of LLM chatbots have suggested that LLM responses may be equivalent or even superior to human doctors in terms of accuracy and empathy, and experiments using complicated case studies suggest that LLMs operate well even outside typical presentations and more common medical conditions [ 4 , 25 , 26 ]. In ophthalmology, GPT-3.5 and GPT-4 have been shown to be capable of providing precise and suitable triage decisions when queried with eye-related symptoms [ 22 , 27 ]. Further work is now warranted in conventional clinical settings.

Second, while the study was sufficiently powered to detect a less than 10% difference in overall performance, the relatively small number of questions in certain categories used for stratification analysis may mask significant differences in performance. Testing LLMs and clinicians with more questions may help establish where LLMs exhibit greater or lesser ability in ophthalmology. Furthermore, researchers using different ways to categorise questions may be able to identify specific strengths and weaknesses of LLMs and doctors which could help guide design of clinical LLM interventions.

Finally, experimental tasks were ‘zero-shot’ in that LLMs were not provided with any examples of correctly answered questions before it was queried with FRCOphth questions from the textbook. This mode of interrogation entails the maximal level of difficulty for LLMs, so it is conceivable that the ophthalmological knowledge and reasoning encoded within these models is actually even greater than indicated by results here [ 1 ]. Future research may seek to fine-tune LLMs by using more domain-specific text during pretraining and fine-tuning, or by providing examples of successfully completed tasks to further improve performance in that clinical task [ 3 ].

Future directions

Autonomous deployment of LLMs is currently precluded by inaccuracy and fact fabrication. Our study found that despite meeting expert standards, state-of-the-art LLMs such as GPT-4 do not match top-performing ophthalmologists [ 28 ]. Moreover, there remain controversial ethical questions about what roles should and should not be assigned to inanimate AI models, and to what extent human clinicians must remain responsible for their patients [ 3 ]. However, the remarkable performance of GPT-4 in ophthalmology examination questions suggests that LLMs may be able to provide useful input in clinical contexts, either to assist clinicians in their day-to-day work or with their education or preparation for examinations [ 3 , 13 , 14 , 27 ]. Further improvement in performance may be obtained by specific fine-tuning of models with high quality ophthalmological text data, requiring curation and deidentification [ 29 ]. GPT-4 may prove especially useful where access to ophthalmologists is limited: provision of advice, diagnosis, and management suggestions by a model with FRCOphth Part 2-level knowledge and reasoning ability is likely to be superior to non-specialist doctors and allied healthcare professionals working without support, as their exposure to and knowledge of eye care is limited [ 27 , 30 , 31 ].

However, close monitoring is essential to avoid mistakes caused by inaccuracy or fact fabrication [ 32 ]. Clinical applications would also benefit from an uncertainty indicator reducing the risk of erroneous decisions [ 7 ]. As LLM performance often correlates with the frequency of query terms’ representation in the model’s training dataset, a simple indicator of ‘familiarity’ could be engineered by calculating the relative frequency of query term representation in the training data [ 7 , 33 ]. Users could appraise familiarity to temper their confidence in answers provided by the LLM, perhaps reducing error. Moreover, ophthalmological applications require extensive validation, preferably with high quality randomised controlled trials to conclusively demonstrate benefit (or lack thereof) conferred to patients by LLM interventions [ 34 ]. Trials should be pragmatic so as not to inflate effect sizes beyond what may generalise to patients once interventions are implemented at scale [ 34 , 35 ]. In addition to patient outcomes, practitioner-related variables should also be considered: interventions aiming to improve efficiency should be specifically tested to ensure that they reduce rather than increase clinicians’ workload [ 3 ].

According to comparisons with expert and trainee doctors, state-of-the-art LLMs are approaching expert-level performance in advanced ophthalmology questions. GPT-4 attains pass-worthy performance in FRCOphth Part 2 questions and exceeds the scores of some expert ophthalmologists. As top-performing doctors exhibit superior scores, LLMs do not appear capable of replacing ophthalmologists, but state-of-the-art models could provide useful advice and assistance to non-specialists or patients where access to eye care professionals is limited [ 27 , 28 ]. Further research is required to design LLM-based interventions which may improve eye health outcomes, validate interventions in clinical trials, and engineer governance structures to regulate LLM applications as they begin to be deployed in clinical settings [ 36 ].

Supporting information

S1 fig. chatgpt performance in questions taken from the whole textbook..

Mosaic plot depicting the overall performance of ChatGPT versions powered by GPT-3.5 and GPT-4 in 360 FRCOphth Part 2 written examination questions. Performance was significantly higher for GPT-4 than GPT-3.5, and was close to mean human examination candidate performance and pass mark set by standard setting and after adjustment.

https://doi.org/10.1371/journal.pdig.0000341.s001

S1 Table. Question characteristics and performance of GPT-3.5 and GPT-4 over the whole textbook.

Similar observations were noted here to the smaller mock examination used for subsequent experiments. GPT-4 performs to a significantly higher standard than GPT-3.5

https://doi.org/10.1371/journal.pdig.0000341.s002

S2 Table. Examination statistics corresponding to FRCOphth Part 2 written examinations sat between July 2017-December 2022.

https://doi.org/10.1371/journal.pdig.0000341.s003

S3 Table. Experience of expert ophthalmologists (E1-E5), ophthalmology trainees (T1-T3), and unspecialised junior doctors (J1-J2) involved in experiments.

https://doi.org/10.1371/journal.pdig.0000341.s004

S4 Table. Results of statistical tests of variation in performance between question subjects and types, for each trialled LLM, expert ophthalmologist, and trainee doctor.

Statistically significant results are highlighted in green.

https://doi.org/10.1371/journal.pdig.0000341.s005

S1 Protocol. Procedures followed by ophthalmologists to grade the output of GPT-3.5 and GPT-4 in terms of accuracy, relevance, and rater-preference of model outputs.

https://doi.org/10.1371/journal.pdig.0000341.s006

Acknowledgments

The authors extend their thanks to Mr Arunachalam Thirunavukarasu (Betsi Cadwaladr University Health Board) for his advice and assistance with recruitment.

• 1. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al. Language Models are Few-Shot Learners. In: Advances in Neural Information Processing Systems [Internet]. Curran Associates, Inc.; 2020 [cited 2023 Jan 30]. p. 1877–901. Available from: https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
• 2. OpenAI. GPT-4 Technical Report [Internet]. arXiv; 2023 [cited 2023 Apr 11]. Available from: http://arxiv.org/abs/2303.08774
• View Article
• PubMed/NCBI
• Google Scholar
• 9. Google. PaLM 2 Technical Report [Internet]. 2023 [cited 2023 May 11]. Available from: https://ai.google/static/documents/palm2techreport.pdf
• 17. Ting DSJ, Steel D. MCQs for FRCOphth Part 2. Oxford University Press; 2020. 253 p.
• 21. Part 2 Written FRCOphth Exam [Internet]. The Royal College of Ophthalmologists. [cited 2023 Jan 30]. Available from: https://www.rcophth.ac.uk/examinations/rcophth-exams/part-2-written-frcophth-exam/