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How to present patient cases

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• Peer review
• Mary Ni Lochlainn , foundation year 2 doctor 1 ,
• Ibrahim Balogun , healthcare of older people/stroke medicine consultant 1
• 1 East Kent Foundation Trust, UK

A guide on how to structure a case presentation

This article contains...

-History of presenting problem

-Medical and surgical history

-Drugs, including allergies to drugs

-Family history

-Social history

-Review of systems

-Findings on examination, including vital signs and observations

-Differential diagnosis/impression

-Investigations

-Management

Presenting patient cases is a key part of everyday clinical practice. A well delivered presentation has the potential to facilitate patient care and improve efficiency on ward rounds, as well as a means of teaching and assessing clinical competence. 1

The purpose of a case presentation is to communicate your diagnostic reasoning to the listener, so that he or she has a clear picture of the patient’s condition and further management can be planned accordingly. 2 To give a high quality presentation you need to take a thorough history. Consultants make decisions about patient care based on information presented to them by junior members of the team, so the importance of accurately presenting your patient cannot be overemphasised.

As a medical student, you are likely to be asked to present in numerous settings. A formal case presentation may take place at a teaching session or even at a conference or scientific meeting. These presentations are usually thorough and have an accompanying PowerPoint presentation or poster. More often, case presentations take place on the wards or over the phone and tend to be brief, using only memory or short, handwritten notes as an aid.

Everyone has their own presenting style, and the context of the presentation will determine how much detail you need to put in. You should anticipate what information your senior colleagues will need to know about the patient’s history and the care he or she has received since admission, to enable them to make further management decisions. In this article, I use a fictitious case to show how you can structure case presentations, which can be adapted to different clinical and teaching settings (box 1).

Box 1: Structure for presenting patient cases

Presenting problem, history of presenting problem, medical and surgical history.

Drugs, including allergies to drugs

Family history

Social history, review of systems.

Findings on examination, including vital signs and observations

Differential diagnosis/impression

Investigations

Case: tom murphy.

You should start with a sentence that includes the patient’s name, sex (Mr/Ms), age, and presenting symptoms. In your presentation, you may want to include the patient’s main diagnosis if known—for example, “admitted with shortness of breath on a background of COPD [chronic obstructive pulmonary disease].” You should include any additional information that might give the presentation of symptoms further context, such as the patient’s profession, ethnic origin, recent travel, or chronic conditions.

“ Mr Tom Murphy is a 56 year old ex-smoker admitted with sudden onset central crushing chest pain that radiated down his left arm.”

In this section you should expand on the presenting problem. Use the SOCRATES mnemonic to help describe the pain (see box 2). If the patient has multiple problems, describe each in turn, covering one system at a time.

Box 2: SOCRATES—mnemonic for pain

Associations

Time course

Exacerbating/relieving factors

“ The pain started suddenly at 1 pm, when Mr Murphy was at his desk. The pain was dull in nature, and radiated down his left arm. He experienced shortness of breath and felt sweaty and clammy. His colleague phoned an ambulance. He rated the pain 9/10 in severity. In the ambulance he was given GTN [glyceryl trinitrate] spray under the tongue, which relieved the pain to 5/10. The pain lasted 30 minutes in total. No exacerbating factors were noted. Of note: Mr Murphy is an ex-smoker with a 20 pack year history”

Some patients have multiple comorbidities, and the most life threatening conditions should be mentioned first. They can also be categorised by organ system—for example, “has a long history of cardiovascular disease, having had a stroke, two TIAs [transient ischaemic attacks], and previous ACS [acute coronary syndrome].” For some conditions it can be worth stating whether a general practitioner or a specialist manages it, as this gives an indication of its severity.

In a surgical case, colleagues will be interested in exercise tolerance and any comorbidity that could affect the patient’s fitness for surgery and anaesthesia. If the patient has had any previous surgical procedures, mention whether there were any complications or reactions to anaesthesia.

“Mr Murphy has a history of type 2 diabetes, well controlled on metformin. He also has hypertension, managed with ramipril, and gout. Of note: he has no history of ischaemic heart disease (relevant negative) (see box 3).”

Box 3: Relevant negatives

Mention any relevant negatives that will help narrow down the differential diagnosis or could be important in the management of the patient, 3 such as any risk factors you know for the condition and any associations that you are aware of. For example, if the differential diagnosis includes a condition that you know can be hereditary, a relevant negative could be the lack of a family history. If the differential diagnosis includes cardiovascular disease, mention the cardiovascular risk factors such as body mass index, smoking, and high cholesterol.

Highlight any recent changes to the patient’s drugs because these could be a factor in the presenting problem. Mention any allergies to drugs or the patient’s non-compliance to a previously prescribed drug regimen.

To link the medical history and the drugs you might comment on them together, either here or in the medical history. “Mrs Walsh’s drugs include regular azathioprine for her rheumatoid arthritis.”Or, “His regular drugs are ramipril 5 mg once a day, metformin 1g three times a day, and allopurinol 200 mg once a day. He has no known drug allergies.”

If the family history is unrelated to the presenting problem, it is sufficient to say “no relevant family history noted.” For hereditary conditions more detail is needed.

“ Mr Murphy’s father experienced a fatal myocardial infarction aged 50.”

Social history should include the patient’s occupation; their smoking, alcohol, and illicit drug status; who they live with; their relationship status; and their sexual history, baseline mobility, and travel history. In an older patient, more detail is usually required, including whether or not they have carers, how often the carers help, and if they need to use walking aids.

“He works as an accountant and is an ex-smoker since five years ago with a 20 pack year history. He drinks about 14 units of alcohol a week. He denies any illicit drug use. He lives with his wife in a two storey house and is independent in all activities of daily living.”

Do not dwell on this section. If something comes up that is relevant to the presenting problem, it should be mentioned in the history of the presenting problem rather than here.

“Systems review showed long standing occasional lower back pain, responsive to paracetamol.”

Findings on examination

Initially, it can be useful to practise presenting the full examination to make sure you don’t leave anything out, but it is rare that you would need to present all the normal findings. Instead, focus on the most important main findings and any abnormalities.

“On examination the patient was comfortable at rest, heart sounds one and two were heard with no additional murmurs, heaves, or thrills. Jugular venous pressure was not raised. No peripheral oedema was noted and calves were soft and non-tender. Chest was clear on auscultation. Abdomen was soft and non-tender and normal bowel sounds were heard. GCS [Glasgow coma scale] was 15, pupils were equal and reactive to light [PEARL], cranial nerves 1-12 were intact, and he was moving all four limbs. Observations showed an early warning score of 1 for a tachycardia of 105 beats/ min. Blood pressure was 150/90 mm Hg, respiratory rate 18 breaths/min, saturations were 98% on room air, and he was apyrexial with a temperature of 36.8 ºC.”

Differential diagnoses

Mentioning one or two of the most likely diagnoses is sufficient. A useful phrase you can use is, “I would like to rule out,” especially when you suspect a more serious cause is in the differential diagnosis. “History and examination were in keeping with diverticular disease; however, I would like to rule out colorectal cancer in this patient.”

Remember common things are common, so try not to mention rare conditions first. Sometimes it is acceptable to report investigations you would do first, and then base your differential diagnosis on what the history and investigation findings tell you.

“My impression is acute coronary syndrome. The differential diagnosis includes other cardiovascular causes such as acute pericarditis, myocarditis, aortic stenosis, aortic dissection, and pulmonary embolism. Possible respiratory causes include pneumonia or pneumothorax. Gastrointestinal causes include oesophageal spasm, oesophagitis, gastro-oesophageal reflux disease, gastritis, cholecystitis, and acute pancreatitis. I would also consider a musculoskeletal cause for the pain.”

This section can include a summary of the investigations already performed and further investigations that you would like to request. “On the basis of these differentials, I would like to carry out the following investigations: 12 lead electrocardiography and blood tests, including full blood count, urea and electrolytes, clotting screen, troponin levels, lipid profile, and glycated haemoglobin levels. I would also book a chest radiograph and check the patient’s point of care blood glucose level.”

You should consider recommending investigations in a structured way, prioritising them by how long they take to perform and how easy it is to get them done and how long it takes for the results to come back. Put the quickest and easiest first: so bedside tests, electrocardiography, followed by blood tests, plain radiology, then special tests. You should always be able to explain why you would like to request a test. Mention the patient’s baseline test values if they are available, especially if the patient has a chronic condition—for example, give the patient’s creatinine levels if he or she has chronic kidney disease This shows the change over time and indicates the severity of the patient’s current condition.

“To further investigate these differentials, 12 lead electrocardiography was carried out, which showed ST segment depression in the anterior leads. Results of laboratory tests showed an initial troponin level of 85 µg/L, which increased to 1250 µg/L when repeated at six hours. Blood test results showed raised total cholesterol at 7.6 mmol /L and nil else. A chest radiograph showed clear lung fields. Blood glucose level was 6.3 mmol/L; a glycated haemoglobin test result is pending.”

Dependent on the case, you may need to describe the management plan so far or what further management you would recommend.“My management plan for this patient includes ACS [acute coronary syndrome] protocol, echocardiography, cardiology review, and treatment with high dose statins. If you are unsure what the management should be, you should say that you would discuss further with senior colleagues and the patient. At this point, check to see if there is a treatment escalation plan or a “do not attempt to resuscitate” order in place.

“Mr Murphy was given ACS protocol in the emergency department. An echocardiogram has been requested and he has been discussed with cardiology, who are going to come and see him. He has also been started on atorvastatin 80 mg nightly. Mr Murphy and his family are happy with this plan.”

The summary can be a concise recap of what you have presented beforehand or it can sometimes form a standalone presentation. Pick out salient points, such as positive findings—but also draw conclusions from what you highlight. Finish with a brief synopsis of the current situation (“currently pain free”) and next step (“awaiting cardiology review”). Do not trail off at the end, and state the diagnosis if you are confident you know what it is. If you are not sure what the diagnosis is then communicate this uncertainty and do not pretend to be more confident than you are. When possible, you should include the patient’s thoughts about the diagnosis, how they are feeling generally, and if they are happy with the management plan.

“In summary, Mr Murphy is a 56 year old man admitted with central crushing chest pain, radiating down his left arm, of 30 minutes’ duration. His cardiac risk factors include 20 pack year smoking history, positive family history, type 2 diabetes, and hypertension. Examination was normal other than tachycardia. However, 12 lead electrocardiography showed ST segment depression in the anterior leads and troponin rise from 85 to 250 µg/L. Acute coronary syndrome protocol was initiated and a diagnosis of NSTEMI [non-ST elevation myocardial infarction] was made. Mr Murphy is currently pain free and awaiting cardiology review.”

Originally published as: Student BMJ 2017;25:i4406

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed

• ↵ Green EH, Durning SJ, DeCherrie L, Fagan MJ, Sharpe B, Hershman W. Expectations for oral case presentations for clinical clerks: opinions of internal medicine clerkship directors. J Gen Intern Med 2009 ; 24 : 370 - 3 . doi:10.1007/s11606-008-0900-x   pmid:19139965 . OpenUrl CrossRef PubMed Web of Science
• ↵ Olaitan A, Okunade O, Corne J. How to present clinical cases. Student BMJ 2010;18:c1539.
• ↵ Gaillard F. The secret art of relevant negatives, Radiopedia 2016; http://radiopaedia.org/blog/the-secret-art-of-relevant-negatives .

Clinical Case

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Clinical case reports have been the earliest form of medical communication. A clinical case report or case study is a means of disseminating new knowledge gained from clinical practice. Medical practitioners often come across patient cases that are different or unusual such as a previously unknown condition, a complication of a known disease, an unusual side effect or adverse response to a mode of treatment, or a new approach to a common medical condition. Thus, a clinical case report is expected to discuss the signs, symptoms, diagnosis, and treatment of a disease.

Clinical case reports are the first-line evidence in medical literature as they present original observations and can be an excellent way for medical students and practitioners to get started with academic writing. Additionally, a published case report is definitely a contribution to medical science and a great addition to a CV.

Informed consent in an ethical requirement for most studies involving humans. It is important to take written consent from the patient before you start writing your case report as all journals will require you to provide patient consent at the time of manuscript submission. In case the patient is a minor, parental consent is required.  For adults who are unable to consent to investigation or treatment, consent of closest family members is required. In general, all case reports include the following components – an abstract, an introduction, a case, and a discussion. 

The abstract should summarize the case, the problem it addresses, and the message it conveys. Abstracts of case studies are usually very short, preferably not more than 150 words. The introduction gives a brief overview of the problem that the case addresses, citing relevant literature where necessary. The introduction generally ends with a single sentence describing the patient and the basic condition that he or she is suffering from.

Case studies are a vehicle for doctors around the world to share their experiences with handling challenging patient cases. These can be valuable sources of information and guidance for clinical practitioners when faced with puzzling or challenging conditions in patients they attend to.

This template will be useful for healthcare professionals and doctors when preparing a report on a new type of disease or a new symptom of a disease. You can structure and present your new patient data. Also, this template can be used for training with interns, which will be conducted by experienced hospital doctors.

Also, this template will be useful for specialists in research centers and pharmaceutical companies. You can use the slides in this template to prepare for a Doctorate Symposium or to speak to medical students.

Clinical Case Template is a stylish and professional template that contains four slides. All slides in the template can be easily customized to suit your corporate color requirements. Clinical Case Template will be useful for doctors and medical professionals, teachers of medical universities. This template’s slides will be a great addition to your professional presentation collection.

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Case Study PowerPoint Templates and Slide Designs for Presentations

Our 100% editable case study presentation templates and slides can help to individuals to prepare great case study presentations. In this section, you can find a collection of Case Study PowerPoint templates and Google Slides templates. From standard slides to creative case study layouts and slide decks, these template presentations are perfect for any company looking to add that extra touch.

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A case study typically involves an in-depth and detailed examination of a specific case or group of cases, within a real-world context. Case studies are very common in medicine, business, politics and education.

For example, in medicine, a case study may focus on an individual patient case. In business, case studies are used to cover a particular firm’s strategy or to present a specific analysis on a business case.

In Politics, case studies can range from a specific one-time event happening over time to the in-depth analysis of decisive moments in history.

When to create a Case Study Template?

When you need in-depth information and understanding of a specific subject, you create a Case Study Template. They are also helpful in describing, comparing, evaluating, and comprehending various aspects of a research problem. They keep your project focused and manageable when you don’t have the time or resources to conduct extensive research.

What do you write in a Case Study Presentation?

A well-planned presentation can help your brand stand out from the crowd. Consider these suggestions as you plan your case study. Add visual interest to your presentation, including charts, graphs, and photos. Use white space to break up your data to make your presentation more memorable. It must also include a workable plan and relevant data. Examine our creative and professional-looking Medical Case Study PowerPoint Template and other templates with appealing layouts and backgrounds.

How to effectively present a Case Study Template?

Create a compelling case study to highlight the essentials, describe your vision clearly and concisely, and demonstrate the project’s value and benefit to the company or client. In addition, the style must be consistent.

Which are the advantages of using a Case Study Template?

The following are the benefits of using a case study template: It easily converts opinion into fact; it is accessible and inexpensive, it can be carried out remotely using a variety of research methodologies and data, and they are also useful when preparing for a Case Study Interview . You can use our Success Story PowerPoint Template to showcase your business success stories.

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• Clinical Case Template

The term clinical case is applied to information on the history of symptoms that a patient suffers during the course of a treatment. It is almost always applied in rare diseases or because the treatment options are complex or new. To provide information regarding this topic, you should consider using this disease care and prevention inspired case report template in your presentations.

This clinical case study template for PowerPoint and Google Slides stands out a lot for its eye-catching images and 100% editable tools. In its 36 slides you can find a background color scheme that is very pleasing to the human eye. In addition, you can make effective and quick presentations, very easily, just download this free ppt file and take it to Canva editing.

Free Clinical Case Template for PowerPoint and Google Slides

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Clinical Radiology Case Presentation: Do’s and Don’ts

Geethu e. punnen.

1 Division of Clinical Radiology, Department of Radiodiagnosis, Christian Medical College, Vellore, Tamil Nadu, India

Shyamkumar N. Keshava

2 Division of Clinical Radiology, Department of Interventional Radiology, Christian Medical College, Vellore, Tamil Nadu, India

Sridhar Gibikote

Clinical case presentation is part of daily routine for doctors to communicate with each other to facilitate learning, and ultimately patient management. Hence, the art of good clinical case presentation is a skill that needs to be mastered. Case presentations are a part of most undergraduate and postgraduate training programs aimed at nurturing oratory and presentation design skills. This article is an attempt at providing a trainee in radiology a guideline to good case presentation skills.

Introduction

Good clinical case presentation is an essential skill to be learnt by all medical professionals. A well-presented clinical case is not only a didactic tool for individual or group learning but also reflects one’s aptitude for clinical reasoning and competency in being able to obtain, process, and organize patient data. These attributes contribute to the ability to communicate important clinical details with other professionals to provide comprehensive patient care. 1 2

The art of how to make a succinct and clear clinical case presentation is a skill that needs to be ingrained into trainees during any postgraduate training. A good case presentation gives the impression of professional competence. In this era, radiologists are involved significantly in guiding the management plan of most patients. During tumor boards and multidisciplinary meetings, radiologists play a pivotal role in presenting imaging findings of a patient to treating teams to brainstorm together the most ideal treatment plan. 3 Learning the art of case presentation will improve our communication with the referring team, whether be it on the phone or as a written report or while presenting in a multidisciplinary meeting. 1

One of the ways to develop good clinical case presentation skills is through regular practice of presenting clinical cases and their imaging findings, discussing the differentials, and narrowing down to the most likely diagnosis, after raising the various diagnostic challenges involved. 4

Many radiology conferences and continuing medical education (CME) provide an opportunity for case presentations to trainees. A clinical case presentation aims at involving the audience in the patient’s story. Perhaps most audience love to play “detective” and as they listen to a clinical case presentation, consciously or unconsciously; they are constructing a differential diagnosis. At the end, not only have they gone through the exercise of clinical reasoning trying to solve the patient’s problem, but also acquired or refreshed their knowledge about the discussed case. A succinct presentation and a confident presenter can drive home a learning point effectively.

The aim of this article was to chalk out a few strategies for especially radiologists in training on how to make a good clinical presentation.

What Defines a Good Presentation?

An ideal presentation is one that contains organized, coherent content that is presented crisply, adhering to the allotted time with an appropriate take-home message.

A simple “step by step” guide to formulating a comprehensive case presentation is as follows:

Step 1: Finding the Right Case to Present

Any case with a learning point is a good case to present. It does not necessarily have to be a rare case. A case can be of interest because it posed a challenge to diagnosis or management or if it is a common condition with an atypical finding, or unusual presentation. It is important to select a case in which imaging has played a role in correct diagnosis or interventional radiology had a role in the management. A case that illustrates novel approaches to known or common conditions is also an example of a case that can be chosen for presentation. 5

Always be alert and on the lookout for interesting cases during routine reporting. Being prospectively involved with the patient will give the advantage of tailoring the imaging appropriately and to understand the various problems the patient is going through as well as empathize with the patient and family. These will contribute significantly in being able to obtain follow-up and hence in the overall completeness of the case. Get in touch with a senior or faculty who can guide you through the workup of the case.

Step 2: Construction of the Presentation

Framing the appropriate title.

The title chosen should capture the attention of the audience. An intriguing title makes the audience want to hear the patient’s story. It can represent the system involved or patient’s presentation or imaging finding. 5 The title must not give away the diagnosis. For example, a title should not state “case of neurofibromatosis.” It takes away the suspense from the presentation. A simple way can be with patient’s clinical presentation, for example, “an unusual cause of back pain,” while a title such as “bubble trouble” induces speculation regarding the case in the audience. This can be a title for cystic lesions in any part of the body.

SAILS Approach

There are five main steps involved in making a case presentation that for the ease of quick recollection has been abbreviated as the “SAILS” approach ( Table 1 ). 6

Introduce in a few lines the patient’s age, gender, and relevant presenting complaints and relevant active medical problems. Specific details such as occupation and region, may be included if it is relevant and will make your listener weigh diagnostic possibilities differently. Relevant history of the illness or treatment received may be presented. The important and relevant laboratory investigation needs to be highlighted ( Fig. 1 ).

Slide without ( A ) and with ( B ) highlighting the relevant laboratory investigations that prevent the audience from not getting lost in all the information.

Making an outline is often the first step to be done. 7 Please mention what was the working diagnosis based on clinical presentation. Do not present unnecessary information that will drag the audience through wrong paths or give misleading clues to throw them off the route to diagnosis.

When presenting radiological imaging, ensure to present it in the chronological order. If the patient has prior imaging done from elsewhere before he or she presents to you, this may also be presented. Always highlight its contribution to the working diagnosis or to the decision on the appropriate imaging to be performed. Never comment on inadequacies in the available images from elsewhere.

Relevant positive and negative imaging findings need to be stated with clear representative images showing the same.

Analyze the Differential by Comparing the Possibilities, Narrowing Down to the Best Possible Diagnosis

Once clinical presentation, laboratory, and imaging findings are presented, the most awaited part of a case presentation begins, which is narrowing down the list of possible differentials.

Relevant imaging findings are presented with positive and negative findings that along with background medical knowledge contribute to the narrowing down of the differential diagnosis.

Present in a table features favoring or against a possible diagnosis. This helps the presenter as well as the audience to analyze various differentials and serves as a guide to arrive at the most likely differential. A tabular column is a crisp, easy to understand method to compare various conditions. Displaying each of the points in Table 1 after the other using animations may be effective in catching the attention of the audience.

A sample tabular column is illustrated in Fig. 2 . One column can mention the points in favor and the next column mentioning the points not in favor of the corresponding differential diagnosis.

A table comparing possible differentials is the ideal method to convey key points.

Integration of Clinical, Imaging, and Histopathological Findings for Final Diagnosis

Most importantly, integration of clinical findings with laboratory and radiological investigations and histopathological findings is key to arrive at a specific diagnosis. Treatment of the patient received and course in the hospital may also be integrated into this penultimate part of the presentation. A summary is a cogent synthesis of the information that reflects your overall thinking about the patient’s clinical and imaging presentation. 8 9

Literature Review

The review of literature adds to the educative value of the presentation. However, it should be kept in mind that a clinical case presentation is not the same as a seminar. Therefore, the review of literature needs to be brief and succinct. Its main purpose is to articulate the lessons learnt from this case and should illustrate how a similar case should be approached in future. 7 It may contain points relevant to the incidence of condition under discussion, diagnostic challenges, approach to diagnosis, and broad management outlines. Always remember to add references at the bottom of each slide while presenting review of literature. Inclusion of case examples from journals or textbooks should be avoided unless there is a significant value addition without extending beyond the allotted time.

Summary and Take-Home Point

It is advisable that the features favoring or against a possible diagnosis be presented in a table. A short of review of literature will draw attention to the incidence, diagnostic challenges, scope of imaging modalities, and recent advances.

Step 3: Go through a Checklist for a Good Clinical Case Presentation in Radiology

Always go through a checklist to ensure all points are covered in your presentation. Table 2 serves as both a summary and a checklist of what to do and what not to do in a radiology presentation.

Few Pertinent Tips

Slide preparation tips.

The rule of thumb when it comes to making a slide for clinical case presentations is to keep it simple. Various presentation mediums such as PowerPoint and Prezi may be used. 10

Running title: It is prudent to have a running title (topic of presentation) on every slide. This would be beneficial to a person who walks into the presentation late after the introductory slides were presented and to some of the audience who may be only partly attentive during the presentation.

For radiological presentations, a dark background with a light font is ideal. Font size of 28 is preferred; a font size less than 24 units should not be used (Microsoft PowerPoint). Choose a font that is crisp and legible on a computer screen from at least 2 m away. “Arial” font is preferable for academic and formal presentations as it is easy to read. Do not choose flowery or wavy fonts as they may be distractive. Let the font type and size be consistent throughout the presentation. Ensure that there are not more than four to five lines on a slide. Matter should be presented in points and not paragraphs. Bullet points or numbering may be used. Ensure that bullets are aligned to the left of the screen as they are easier to read. Highlight using a separate color or animation, the most important part of these points. The audience can be kept on their feet by creating suspense using nondistractive progressive transitions. Avoid using sounds during transitions. Figs. 3 and ​ and4 show 4 show the examples of the do and don’ts of slide formatting, respectively.

Slide formatting: Good practices for an ideal slide.

Slide formatting: Poor practices of slide formatting.

Most importantly, a carefully created presentation should not have grammatical errors, typographical errors such as extra spaces or inappropriate punctuations. Avoid using all capitals unless it is an acronym.

Images within a Clinical Presentation

It is preferred to have images on a slide with relevant text and labeling by the side. Relevant clinical photographs or videos add color to any presentation. If any clinical photographs are shown, ensure patient anonymity by covering the eyes ( Fig. 5 ). The dignity of the patient should be maintained. Always mention that consent was obtained from the patient for taking the photograph and using it for educational purposes.

Important to maintain patient anonymity by covering their eyes (black box) in all clinical images. White plaque like lesions are shown along the left lateral border of tongue (white arrow).

Radiological images are indispensable in a radiological clinical case presentation. Images should be exhibited with care ensuring that anything mentioned in the corner of the images does not reveal patient identity. All images should be labeled appropriately. Complete label for an image includes an indication of modality and specific sequence of representative image. Findings may be highlighted by using arrows ( Fig. 6 ). Images when borrowed from a textbook, article, or a colleague should be acknowledged on the slide. This applies to data or any information as well. References should be included in the bottom of the slide when applicable.

Images need to be labeled appropriately indicating modality, plane of imaging, and specific window/sequence. This would complement the oral presentation and will aid better time management. Ensure that every slide has a running title.

The Art of Presenting a Clinical Case

The above paragraphs primarily dealt with the content and formatting of the content to make a good clinical case presentation. But this is only half the challenge. The other half is delivering the presentation in the most effective way.

Almost all podium presentations are time-bound. Most often, especially in radiological society conferences or CME, 6 minutes is the time allotted. One effective way of presentation is the modification of the Pecha Kucha presentation technique where typically individuals are given 6 minutes and 40 seconds to display or explain their ideas or work. Pecha Kucha is a Japanese word that translates into “chitchat.” Traditionally, a presenter shows 20 slides, each for 20 seconds but various modifications are being used these days for business meetings, education, or even display of art and music. 11 It is also believed that up to 7 minutes is the maximum length of time a listener can give active and undivided attention. 12

It takes significant practice and finesse to be able to tailor and complete a presentation within this time in front of an audience. Nobody enjoys a talk that goes beyond the allotted time and often the audience loses interest if it is too dragging. In a competitive setting, nonadherence to allotted timing can result in negative marking and hence not being able to achieve top positions.

Delivery of a Case Presentation

Fear of public speaking, also known as “glossophobia,” is widely prevalent. This fear can be overcome by extensive preparation, organization of your thoughts, and repetitive practice. Do not present too fast. Instead add short pauses between points. If the presenter has difficulty in the presentation language (e.g., English), make short sentences. Watch and learn from other experienced speakers how they present a talk. Imbibe the good qualities and avoid the mistakes they make.

Whenever you get a chance to present and discuss in a multidisciplinary meeting, utilize it as an opportunity to improvise communication skills.

It is important to appear calm and relaxed. Record your talk and listen to it, and evaluate it making notes on how you can improve it. Presentation in front of a mirror paying attention to facial gestures, body language is an option of practicing. However, presenting to someone who will be completely honest with you in their critique is better. It can be a friend, family member, or your faculty in charge. Presenting to someone you are comfortable with in the beginning will boost your confidence. Be open to their feedback.

Practice to be poised and present with clear articulation, proper volume, steady rate, good posture, eye contact, enthusiasm, confidence and to complete within the allotted time. Make sure that you look at the audience. It is important to not read from the slides and avoid using distractors such as “uhs uhms and aahs” during the presentation. With each presentation practice, one becomes better than the previous time.

Preparedness for Questions and Discussion

An interesting case presentation always leads to a short discussion or questions from the faculty or audience. The presenter needs to be equipped with adequate knowledge about the case and the condition being discussed. It is important to read about the case and its background extensively. Always be truthful and avoid guess work while answering questions. Remember, you do not necessarily have to be able to answer every question. If you do not know the answer to a question, without wasting the time of the audience, indicate that you are not aware of the answer and need to read up about it.

Finally, at the time of presentation, be prepared if things go different from anticipation. Please ignore minor errors, and concentrate on the remaining presentation. One should not be distracted by technical glitches related to the audio or display. Checking the presentation compatibility with the audio-visual aid and having a backup storage of the presentation are essential. Do not express any unpleasantness regarding technical problems. In case if there is shortage of time due to any technical problems, please switch over to the summary slide and do not compromise with take-home message.

A well-delivered case presentation will facilitate patient care, act as a stimulus for timely intervention, and help identify individual and group learning needs. Case presentations are also used as a tool for assessing clinical competencies at undergraduate and postgraduate level and serve as a tool for teaching. Regular clinical case presentations, under the guidance of faculty, should be incorporated into radiology resident training. We hope this review acts as a guideline that details what to do and what not to do during a clinical case presentation in radiology.

Conflicts of InterestFinancial Support and Sponsorship There are no conflicts of interest.

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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

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• 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
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• 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/

Evidence Review of the Adverse Effects of COVID-19 Vaccination and Intramuscular Vaccine Administration

Vaccines are a public health success story, as they have prevented or lessened the effects of many infectious diseases. To address concerns around potential vaccine injuries, the Health Resources and Services Administration (HRSA) administers the Vaccine Injury Compensation Program (VICP) and the Countermeasures Injury Compensation Program (CICP), which provide compensation to those who assert that they were injured by routine vaccines or medical countermeasures, respectively. The National Academies of Sciences, Engineering, and Medicine have contributed to the scientific basis for VICP compensation decisions for decades.

HRSA asked the National Academies to convene an expert committee to review the epidemiological, clinical, and biological evidence about the relationship between COVID-19 vaccines and specific adverse events, as well as intramuscular administration of vaccines and shoulder injuries. This report outlines the committee findings and conclusions.

Read Full Description

• Digital Resource: Evidence Review of the Adverse Effects of COVID-19 Vaccination
• Digital Resource: Evidence Review of Shoulder Injuries from Intramuscular Administration of Vaccines
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