reflection about nature essay

The Beauty About The Nature

To go into solitude, a man needs to retire as much from his chamber as from society. I am not solitary whilst I read and write, though nobody is with me. But if a man would be alone, let him look at the stars. The rays that come from those heavenly worlds, will separate between him and what he touches. One might think the atmosphere was made transparent with this design, to give man, in the heavenly bodies, the perpetual presence of the sublime. Seen in the streets of cities, how great they are! If the stars should appear one night in a thousand years, how would men believe and adore; and preserve for many generations the remembrance of the city of God which had been shown! But every night come out these envoys of beauty and light the universe with their admonishing smile.

The Stars Awaken a Certain Reverence, Because Though Always Present, They Are Inaccessible;

but all natural objects make a kindred impression when the mind is open to their influence. Nature never wears a mean appearance. Neither does the wisest man extort her secret, and lose his curiosity by finding out all her perfection. Nature never became a toy to a wise spirit. The flowers, the animals, the mountains, reflected the wisdom of his best hour, as much as they had delighted the simplicity of his childhood. When we speak of nature in this manner, we have a distinct but most poetical sense in the mind. We mean the integrity of impression made by manifold natural objects. It is this which distinguishes the stick of timber of the wood-cutter, from the tree of the poet . The charming landscape which I saw this morning, is indubitably made up of some twenty or thirty farms. Miller owns this field, Locke that, and Manning the woodland beyond. But none of them owns the landscape. There is a property in the horizon which no man has but he whose eye can integrate all the parts, that is, the poet . This is the best part of these men's farms, yet to this, their warranty deeds give no title. To speak truly, few adult persons can see nature. Most persons do not see the sun. At least they have a very superficial seeing. The sun illuminates only the eye of the man but shines into the eye and the heart of the child.

The lover of nature is he whose inward and outward senses are still truly adjusted to each other;

who has retained the spirit of infancy even into the era of manhood. His intercourse with heaven and earth becomes part of his daily food. In the presence of nature, a wild delight runs through the man, in spite of real sorrows. Nature says, — he is my creature, and maugre all his impertinent griefs, he shall be glad with me. Not the sun or the summer alone, but every hour and season yields its tribute of delight; for every hour and change corresponds to and authorizes a different state of the mind, from breathless noon to grimmest midnight.

Nature is a setting that fits equally well a comic or a mourning piece. In good health, the air is a cordial of incredible virtue. Crossing a bare common, in snow puddles, at twilight, under a clouded sky, without having in my thoughts any occurrence of special good fortune, I have enjoyed a perfect exhilaration. I am glad to the brink of fear. In the woods too, a man casts off his years, as the snake his slough, and at what period soever of life, is always a child. In the woods, is perpetual youth. Within these plantations of God, a decorum and sanctity reign, a perennial festival is dressed, and the guest sees not how he should tire of them in a thousand years. In the woods, we return to reason and faith.

There I feel that nothing can befall me in life,

— no disgrace, no calamity, (leaving me my eyes,) which nature cannot repair. Standing on the bare ground, — my head bathed by the blithe air, and uplifted into infinite space, — all mean egotism vanishes. I become a transparent eye-ball; I am nothing; I see all; the currents of the Universal Being circulate through me; I am part or particle of God. The name of the nearest friend sounds then foreign and accidental: to be brothers, to be acquaintances, — master or servant, is then a trifle and a disturbance. I am the lover of uncontained and immortal beauty. In the wilderness, I find something more dear and connate than in streets or villages. In the tranquil landscape, and especially in the distant line of the horizon, man beholds somewhat as beautiful as his own nature.

The greatest delight which the fields and woods minister, is the suggestion of an occult relation between man and the vegetable.

I am not alone and unacknowledged. They nod to me, and I to them. The waving of the boughs in the storm is new to me and old. It takes me by surprise, and yet is not unknown. Its effect is like that of a higher thought or a better emotion coming over me, when I deemed I was thinking justly or doing right.

Yet it is certain that the power to produce this delight, does not reside in nature, but in man, or in a harmony of both. It is necessary to use these pleasures with great temperance. For, nature is not always tricked in holiday attire, but the same scene which yesterday breathed perfume and glittered as for the frolic of the nymphs, is overspread with melancholy today. Nature always wears the colors of the spirit. To a man laboring under calamity, the heat of his own fire hath sadness in it. Then, there is a kind of contempt of the landscape felt by him who has just lost by death a dear friend. The sky is less grand as it shuts down over less worth in the population.

Chapter I from Nature , published as part of Nature; Addresses and Lectures

What Is The Meaning Behind Nature, The Poem?

Emerson often referred to nature as the "Universal Being" in his many lectures. It was Emerson who deeply believed there was a spiritual sense of the natural world which felt was all around him.

Going deeper still in this discussion of the "Universal Being", Emerson writes, "The aspect of nature is devout. Like the figure of Jesus, she stands with bended head, and hands folded upon the breast. The happiest man is he who learns from nature the lesson of worship."

It's common sense that "nature" is everything you see that is NOT man-made, or changed by man (trees, foliage, mountains, etc.), but Emerson reminds us that nature was set forth to serve man. This is the essence of human will, for man to harness nature. Every object in nature has its own beauty. Therefore, Emerson advocates to view nature as a reality by building your own world and surrounding yourself with natural beauty.

• The purpose of science is to find the theory of nature.
• Nature wears the colors of the Spirit.
• A man is fed, not to fill his belly, but so he may work.
• Each natural action is graceful.

"Material objects are necessarily kinds of scoriae of the substantial thoughts of the Creator, which must always preserve an exact relation to their first origin; in other words, visible nature must have a spiritual and moral side."

This quote is cited in numerous works and it is attributed to a "French philosopher." However, no name can be found in association with this quote.

What is the main point of Nature, by Emerson?

The central theme of Emerson's famous essay "Nature" is the harmony that exists between the natural world and human beings. In "Nature," Ralph Waldo Emerson contends that man should rid himself of material cares and instead of being burdened by unneeded stress, he can enjoy an original relation with the universe and experience what Emerson calls "the sublime."

What is the central idea of the essay Nature, by Emerson?

For Emerson, nature is not literally God but the body of God’s soul. ”Nature,” he writes, is “mind precipitated.” Emerson feels that to realize one’s role in this respect fully is to be in paradise (similar to heaven itself).

What is Emerson's view of the Nature of humans?

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Ralph Waldo Emerson left the ministry to pursue a career in writing and public speaking. Emerson became one of America's best known and best-loved 19th-century figures. More About Emerson

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"Every man has his own courage, and is betrayed because he seeks in himself the courage of other persons." – Ralph Waldo Emerson

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Guide on How to Write a Reflection Paper with Free Tips and Example

A reflection paper is a very common type of paper among college students. Almost any subject you enroll in requires you to express your opinion on certain matters. In this article, we will explain how to write a reflection paper and provide examples and useful tips to make the essay writing process easier.

Reflection papers should have an academic tone yet be personal and subjective. In this paper, you should analyze and reflect upon how an experience, academic task, article, or lecture shaped your perception and thoughts on a subject.

Here is what you need to know about writing an effective critical reflection paper. Stick around until the end of our guide to get some useful writing tips from the writing team at EssayPro — a research paper writing service

What Is a Reflection Paper

A reflection paper is a type of paper that requires you to write your opinion on a topic, supporting it with your observations and personal experiences. As opposed to presenting your reader with the views of other academics and writers, in this essay, you get an opportunity to write your point of view—and the best part is that there is no wrong answer. It is YOUR opinion, and it is your job to express your thoughts in a manner that will be understandable and clear for all readers that will read your paper. The topic range is endless. Here are some examples: whether or not you think aliens exist, your favorite TV show, or your opinion on the outcome of WWII. You can write about pretty much anything.

There are three types of reflection paper; depending on which one you end up with, the tone you write with can be slightly different. The first type is the educational reflective paper. Here your job is to write feedback about a book, movie, or seminar you attended—in a manner that teaches the reader about it. The second is the professional paper. Usually, it is written by people who study or work in education or psychology. For example, it can be a reflection of someone’s behavior. And the last is the personal type, which explores your thoughts and feelings about an individual subject.

However, reflection paper writing will stop eventually with one very important final paper to write - your resume. This is where you will need to reflect on your entire life leading up to that moment. To learn how to list education on resume perfectly, follow the link on our dissertation writing services .

Unlock the potential of your thoughts with EssayPro . Order a reflection paper and explore a range of other academic services tailored to your needs. Dive deep into your experiences, analyze them with expert guidance, and turn your insights into an impactful reflection paper.

Free Reflection Paper Example

Now that we went over all of the essentials about a reflection paper and how to approach it, we would like to show you some examples that will definitely help you with getting started on your paper.

Reflection Paper Format

Reflection papers typically do not follow any specific format. Since it is your opinion, professors usually let you handle them in any comfortable way. It is best to write your thoughts freely, without guideline constraints. If a personal reflection paper was assigned to you, the format of your paper might depend on the criteria set by your professor. College reflection papers (also known as reflection essays) can typically range from about 400-800 words in length.

Here’s how we can suggest you format your reflection paper:

How to Start a Reflection Paper

The first thing to do when beginning to work on a reflection essay is to read your article thoroughly while taking notes. Whether you are reflecting on, for example, an activity, book/newspaper, or academic essay, you want to highlight key ideas and concepts.

You can start writing your reflection paper by summarizing the main concept of your notes to see if your essay includes all the information needed for your readers. It is helpful to add charts, diagrams, and lists to deliver your ideas to the audience in a better fashion.

After you have finished reading your article, it’s time to brainstorm. We’ve got a simple brainstorming technique for writing reflection papers. Just answer some of the basic questions below:

• How did the article affect you?
• How does this article catch the reader’s attention (or does it all)?
• Has the article changed your mind about something? If so, explain how.
• Has the article left you with any questions?
• Were there any unaddressed critical issues that didn’t appear in the article?
• Does the article relate to anything from your past reading experiences?
• Does the article agree with any of your past reading experiences?

Here are some reflection paper topic examples for you to keep in mind before preparing to write your own:

• How my views on rap music have changed over time
• My reflection and interpretation of Moby Dick by Herman Melville
• Why my theory about the size of the universe has changed over time
• How my observations for clinical psychological studies have developed in the last year

The result of your brainstorming should be a written outline of the contents of your future paper. Do not skip this step, as it will ensure that your essay will have a proper flow and appropriate organization.

Another good way to organize your ideas is to write them down in a 3-column chart or table.

Do you want your task look awesome?

If you would like your reflection paper to look professional, feel free to check out one of our articles on how to format MLA, APA or Chicago style

Writing a Reflection Paper Outline

Reflection paper should contain few key elements:

Introduction

Your introduction should specify what you’re reflecting upon. Make sure that your thesis informs your reader about your general position, or opinion, toward your subject.

• State what you are analyzing: a passage, a lecture, an academic article, an experience, etc...)
• Briefly summarize the work.
• Write a thesis statement stating how your subject has affected you.

One way you can start your thesis is to write:

Example: “After reading/experiencing (your chosen topic), I gained the knowledge of…”

Body Paragraphs

The body paragraphs should examine your ideas and experiences in context to your topic. Make sure each new body paragraph starts with a topic sentence.

Your reflection may include quotes and passages if you are writing about a book or an academic paper. They give your reader a point of reference to fully understand your feedback. Feel free to describe what you saw, what you heard, and how you felt.

Example: “I saw many people participating in our weight experiment. The atmosphere felt nervous yet inspiring. I was amazed by the excitement of the event.”

As with any conclusion, you should summarize what you’ve learned from the experience. Next, tell the reader how your newfound knowledge has affected your understanding of the subject in general. Finally, describe the feeling and overall lesson you had from the reading or experience.

There are a few good ways to conclude a reflection paper:

• Tie all the ideas from your body paragraphs together, and generalize the major insights you’ve experienced.
• Restate your thesis and summarize the content of your paper.

We have a separate blog post dedicated to writing a great conclusion. Be sure to check it out for an in-depth look at how to make a good final impression on your reader.

Need a hand? Get help from our writers. Edit, proofread or buy essay .

How to Write a Reflection Paper: Step-by-Step Guide

Step 1: create a main theme.

After you choose your topic, write a short summary about what you have learned about your experience with that topic. Then, let readers know how you feel about your case — and be honest. Chances are that your readers will likely be able to relate to your opinion or at least the way you form your perspective, which will help them better understand your reflection.

For example: After watching a TEDx episode on Wim Hof, I was able to reevaluate my preconceived notions about the negative effects of cold exposure.

Step 2: Brainstorm Ideas and Experiences You’ve Had Related to Your Topic

You can write down specific quotes, predispositions you have, things that influenced you, or anything memorable. Be personal and explain, in simple words, how you felt.

For example: • A lot of people think that even a small amount of carbohydrates will make people gain weight • A specific moment when I struggled with an excess weight where I avoided carbohydrates entirely • The consequences of my actions that gave rise to my research • The evidence and studies of nutritional science that claim carbohydrates alone are to blame for making people obese • My new experience with having a healthy diet with a well-balanced intake of nutrients • The influence of other people’s perceptions on the harm of carbohydrates, and the role their influence has had on me • New ideas I’ve created as a result of my shift in perspective

Step 3: Analyze How and Why These Ideas and Experiences Have Affected Your Interpretation of Your Theme

Pick an idea or experience you had from the last step, and analyze it further. Then, write your reasoning for agreeing or disagreeing with it.

For example, Idea: I was raised to think that carbohydrates make people gain weight.

Analysis: Most people think that if they eat any carbohydrates, such as bread, cereal, and sugar, they will gain weight. I believe in this misconception to such a great extent that I avoided carbohydrates entirely. As a result, my blood glucose levels were very low. I needed to do a lot of research to overcome my beliefs finally. Afterward, I adopted the philosophy of “everything in moderation” as a key to a healthy lifestyle.

For example: Idea: I was brought up to think that carbohydrates make people gain weight. Analysis: Most people think that if they eat any carbohydrates, such as bread, cereal, and sugar, they will gain weight. I believe in this misconception to such a great extent that I avoided carbohydrates entirely. As a result, my blood glucose levels were very low. I needed to do a lot of my own research to finally overcome my beliefs. After, I adopted the philosophy of “everything in moderation” as a key for having a healthy lifestyle.

Step 4: Make Connections Between Your Observations, Experiences, and Opinions

Try to connect your ideas and insights to form a cohesive picture for your theme. You can also try to recognize and break down your assumptions, which you may challenge in the future.

There are some subjects for reflection papers that are most commonly written about. They include:

• Book – Start by writing some information about the author’s biography and summarize the plot—without revealing the ending to keep your readers interested. Make sure to include the names of the characters, the main themes, and any issues mentioned in the book. Finally, express your thoughts and reflect on the book itself.
• Course – Including the course name and description is a good place to start. Then, you can write about the course flow, explain why you took this course, and tell readers what you learned from it. Since it is a reflection paper, express your opinion, supporting it with examples from the course.
• Project – The structure for a reflection paper about a project has identical guidelines to that of a course. One of the things you might want to add would be the pros and cons of the course. Also, mention some changes you might want to see, and evaluate how relevant the skills you acquired are to real life.
• Interview – First, introduce the person and briefly mention the discussion. Touch on the main points, controversies, and your opinion of that person.

Writing Tips

Everyone has their style of writing a reflective essay – and that's the beauty of it; you have plenty of leeway with this type of paper – but there are still a few tips everyone should incorporate.

Before you start your piece, read some examples of other papers; they will likely help you better understand what they are and how to approach yours. When picking your subject, try to write about something unusual and memorable — it is more likely to capture your readers' attention. Never write the whole essay at once. Space out the time slots when you work on your reflection paper to at least a day apart. This will allow your brain to generate new thoughts and reflections.

• Short and Sweet – Most reflection papers are between 250 and 750 words. Don't go off on tangents. Only include relevant information.
• Clear and Concise – Make your paper as clear and concise as possible. Use a strong thesis statement so your essay can follow it with the same strength.
• Maintain the Right Tone – Use a professional and academic tone—even though the writing is personal.
• Cite Your Sources – Try to cite authoritative sources and experts to back up your personal opinions.
• Proofreading – Not only should you proofread for spelling and grammatical errors, but you should proofread to focus on your organization as well. Answer the question presented in the introduction.

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Reflection About Nature Vs Nurture Theories

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Digging Deep into Purpose and Importance of Reflective Essay

Reflection writing is a powerful tool for students and professionals as they offer a unique opportunity for self-exploration, growth, and understanding. This guide on the importance and purpose of  reflective essays  aims to change your perception of writing and shed light on the many benefits of incorporating reflection into your life. With our amazing  paper help  resources and expert guidance, you can master the art of reflective essay writing and unlock your full potential.

Table of Contents

What is a Reflective Essay?

A reflective essay is a type of writing that allows the author to explore their thoughts, feelings, and experiences in a structured and analytical manner. This form of writing encourages critical thinking and personal growth by examining the author’s experiences, thoughts, actions, and reactions. 

Reflective essays often focus on personal development, learning experiences, or the impact of specific events on the author’s life. However, reflection writing is also used for  college essays  or other forms of academic writing.

Types of Reflection Writing

Reflection essays come in various forms, each with its unique focus and purpose. In this note, we will delve into five types of reflective writing;

Personal Reflective Writing

Professional reflection, academic reflective essay, creative reflection.

• Social or Cultural Reflection Writing

Understanding these different approaches will enable you to choose the most suitable reflection essay type for your needs and make your writing more coherent, insightful and trustworthy.

Journaling, manifestation dairies, and written meditations are common ideas. But would you believe these are all forms and branches of personal reflection writing?

Personal reflection essays explore what you’re going through, emotionally, mentally, and provide insights. These could be about their learning, inner conflicts, resolutions and growth.

This type of reflective writing allows individuals to examine their values, beliefs, and actions, fostering self-awareness and personal development. 

Personal reflection essays may focus on topics such as significant life events, personal challenges, or the impact of relationships on one’s identity and growth.

Professional reflection writing is common in academic or workplace settings. They involve analyzing personal and professional skills and challenges and identifying areas for improvement. 

This reflection essay encourages individuals to examine their professional experiences, decisions, and outcomes, fostering critical thinking and problem-solving skills. 

Professional reflection essays may focus on workplace conflicts, leadership experiences, or developing specific professional competencies.

As a  college paper writing service  platform, we know that most students are intimidated by reflective essay writing. In an academic setting, the reflection essay blurs the lines between informal and formal writing. 

You might be assigned an essay account of your experience with an event, but you’ll still need to follow strict rules of academic writing, i.e., formatting or  organizing a paper . 

Academic reflective writing involves analyzing and evaluating academic materials, such as readings, lectures, or research projects, and connecting them to personal experiences or broader concepts. 

It encourages students to engage with course content on a deeper level, fostering a better understanding of the material and its relevance to their lives and future careers.

For example:

You can be assigned to write a reflection essay on  modernism in literature . You’d have to write your thoughts and observations about this era. Still, you must follow the rules like citation, proper referencing, and contextual analysis of the ideas presented in that era. 

Creative reflection essays are often utilized in artistic or creative fields, allowing individuals to examine their creative process, inspirations, and outcomes. 

This reflective writing fosters self-awareness, critical thinking, and artistic growth, enabling individuals to explore their creative motivations, challenges, and successes. 

Creative reflection essays may focus on topics such as the development of a specific artistic project, the influence of personal experiences on one’s creative work, or the role of collaboration in the creative process.

Social or Cultural Reflection

These reflection essays focus on exploring and understanding social or cultural phenomena. It involves analyzing personal experiences, observations, or interactions with others and reflecting on their significance and broader societal implications. 

Social or cultural reflection essays encourage individuals to engage with the world around them, fostering empathy, critical thinking, and a deeper understanding of social and cultural issues. 

These essays may focus on topics such as the impact of social media on interpersonal relationships, the role of cultural identity in shaping one’s worldview, or the challenges of navigating diverse social environments.

Students must grasp all of these forms of reflective essay writing. Understanding the different types of reflective writing and their unique purposes is required for crafting effective reflection essays.

By selecting the most appropriate reflection essay type for your needs, you can create a coherent, understandable, and persuasive piece of writing that fosters personal and professional growth.

Reflective writing offers a valuable opportunity for self-exploration, critical thinking, and meaningful learning, whether you are exploring your personal experiences, professional challenges, academic materials, creative endeavors, or social and cultural phenomena.

 What is the Purpose of Reflective Essay Writing?

The amazing thing about reflective essay writing is that, although we have discussed its few meaningful purposes, there’s still a long list to cover. 

These numerous goals are particularly for students dealing with academic stress and professionals experiencing work-related challenges. Here are 8 key purposes of reflective paper writing. 

• Self-awareness : Reflective essays help individuals develop a deeper understanding of themselves, their values, beliefs, and emotions.
• Critical thinking : It encourages the examination of one’s thoughts and experiences, fostering the development of critical thinking skills.
• Personal growth : Reflection writing enables individuals to learn from their experiences, identify areas for improvement, and set goals for personal development.
• Problem-solving : Reflection essay writing can help identify the root causes of problems and generate potential solutions.
• Emotional processing : Writing about emotional experiences can help individuals process and cope with their feelings.
• Learning from mistakes : Reflection writing encourages individuals to examine their failures, learn from them, and develop resilience.
• Enhancing communication skills : Reflective writing helps improve written communication skills and promotes effective self-expression.
• Empathy development:  The reflective essays can foster empathy by encouraging individuals to consider the perspectives and experiences of others.

Why Is Reflection Essay Important for Students?

Reflection writing is a crucial aspect of a student’s academic journey. Here are several reasons why reflection writing is essential for students:

Promotes Self-Awareness

Self-awareness in a student involves recognizing their academic learning style, studying habits, strengths, and weaknesses. Reflective Writing plays a crucial role in building self-awareness in students. 

Most students struggle with consulting adults or peers with issues like processing information, retaining knowledge, and solving problems effectively. They have a hard time coming to terms with certain values, beliefs, goals, and emotions. 

And an even harder time in exploring and creating their identities. Practicing reflective thought writing enables students to make informed decisions, set realistic goals, and develop healthy relationships. 

Self-aware students take ownership of their learning and personal development, seeking feedback, reflecting on experiences, and adapting their approaches. Thus, reflective essay writing contributes to effective communication, collaboration, and navigating challenges.

Develops Critical Thinking Skills

Reflective writing develops critical thinking skills in students by prompting them to analyze and evaluate their thoughts, experiences, and perspectives. 

It encourages questioning assumptions, considering alternative viewpoints, and making informed judgments. Students practice higher-order thinking skills such as analysis, synthesis, and evaluation through reflection. 

They learn to articulate their ideas clearly and support them with evidence. Overall, reflective writing plays a crucial role in fostering critical thinking by promoting deep thinking, evaluation of evidence, and effective communication of thoughts.

Practical Academic Stress Dealing

Reflective writing induces practical academic stress dealing in students by improving self-expression, facilitating self-composition, promoting goal-setting and problem-solving, enhancing writing skills, and fulfilling academic requirements. These benefits empower students to navigate their academic challenges more effectively and succeed in their studies.

• Improves self-expression : Reflection writing helps students enhance their written communication skills and promotes effective self-expression, which is vital for academic success and personal growth.
• Self-composition:  Reflective writing allows students to compose their thoughts and ideas in a structured and coherent manner. It encourages them to organize their reflections, leading to clearer and more articulate writing.
• Setting better goals:  Engaging in reflective writing prompts students to set better academic goals. It helps them assess their strengths and weaknesses, identify areas for improvement, and establish realistic objectives for their studies.
• Problem-solving : Reflective writing encourages students to analyze academic challenges and develop strategies to overcome them. It fosters critical thinking and problem-solving skills, enabling students to tackle obstacles and find effective solutions.
• Organized and better-polished writing skills : Regular practice of reflective writing hones students’ writing skills. It enhances their ability to structure their thoughts, use appropriate language, and present coherent arguments, leading to more organized and polished writing.
• Fulfills academic requirements : Reflective essays are often assigned as part of the coursework, and students need to write them to meet academic requirements. Developing reflection writing skills ensures students can effectively complete these assignments while meeting the expectations of their instructors.

Navigating Life Transitions 

Students often face significant life transitions, such as moving away from home or choosing a career path. Reflection essay writing can help them process these changes, identify their goals, and make informed decisions. 

By engaging in reflective writing, students can explore their thoughts, emotions, and experiences related to the transitions they are facing. This process allows them to gain clarity, understand their values and aspirations, and evaluate different options. 

Reflective writing is a valuable tool for self-reflection and self-discovery, empowering students to navigate life’s transitions with a deeper understanding of themselves and their desired path forward.

Addresses Emotional and Mental Conflicts 

Students may experience emotional or  mental conflicts  due to various factors, such as relationships, academic pressure, or personal issues. Reflection writing provides an opportunity to explore and resolve these conflicts, promoting mental well-being. 

By engaging in reflective writing, students can express and process their emotions, gain insights into their turmoil’s underlying causes, and develop coping and problem-solving strategies. It offers a safe and therapeutic outlet for self-expression, self-reflection, and self-care. 

Reflective essay writing empowers students to navigate their emotional and mental challenges, fostering resilience, self-awareness, and overall psychological well-being.

Balancing Work and Studies 

Many students juggle work and studies simultaneously. Reflection writing can help them assess their time management and prioritization skills, identify areas for improvement, and develop strategies to maintain a healthy work-study balance.

Encourages Empathy Development

Reflective essays can foster empathy by encouraging students to consider the perspectives and experiences of others, an essential skill for building strong relationships and navigating diverse social environments.

Reflection Essay Writing Format

Reflection essays require a structured approach to ensure coherence and clarity in presenting one’s thoughts, emotions, and experiences. This detailed tutorial will provide an overview of the reflection essay writing format and offer instructions on how to apply APA and  MLA formatting to your reflection essay.

A well-structured reflection essay typically includes the following elements:

• Introduction : Provide an overview of the topic or experience you will be reflecting on and briefly explain its significance.
• Description : Describe the experience or event in detail, including relevant facts, feelings, and observations.
• Analysis : Examine your thoughts, emotions, and reactions to the experience, and consider the factors that influenced your response.
• Evaluation : Assess the impact of the experience on your personal growth, learning, or development and discuss any lessons learned.
• Conclusion : Summarize your reflections, reiterate the significance of the experience, and discuss any future implications or goals.

APA Formatting for Reflection Essays

The American Psychological Association ( APA ) formatting style is commonly used in social sciences and education. Here are the key formatting instructions for a reflection essay in  APA  style:

• Title Page : Include a title page with the title of your essay, your name, and the name of your institution, all centered and double-spaced.
• Running Head : Include a running head on the top-left corner of each page, consisting of a shortened version of your essay title (in capital letters) and the page number.
• Font and Spacing : Use a 12-point, Times New Roman font with double-spacing throughout the essay.
• Margins : Set 1-inch margins on all sides of the page.
• Headings : Use headings to organize your essay, with level one headings centered and bold, level two headings flush left and bold, and level three headings flush left, bold, and italicized.
• Citations :  If you refer to any external sources, use in-text citations with the author’s last name and the publication year in parentheses.
• Reference List : Include a reference list at the end of your essay, with a centered and bold “References” heading, and list all cited sources in alphabetical order by the author’s last name.

MLA Formatting for Reflection Essays

The Modern Language Association (MLA) formatting style is commonly used in humanities and liberal arts. Here are the key formatting instructions for a reflection essay in MLA style:

• Header :  Include a header on the top-right corner of each page, consisting of your last name and the page number.
• Title :  Center the title of your essay at the top of the first page, using standard capitalization. Do not underline, italicize, or place the title in quotation marks.
• Indentation : Indent the first line of each paragraph by 0.5 inches.
• Citations :  If you refer to any external sources, use in-text citations with the author’s last name and the page number in parentheses.
• Works Cited : Include a Works Cited page at the end of your essay, with a centered “Works Cited” heading, and list all cited sources in alphabetical order by the author’s last name.

Topics for Reflection Essays

Reflective essay topics can vary widely, depending on the individual’s experiences, interests, and goals. Some examples of reflective essay topics include:

• A significant personal experience and its impact on your life.
• A challenging academic or professional situation and the lessons learned.
• A personal or professional failure and how it has shaped your development.
• A meaningful relationship or encounter with someone who has influenced your perspective.
• A volunteer or community service experience and its effect on your values or beliefs.
• A time when you faced a moral or ethical dilemma and how you resolved it.
• A personal or professional goal and the steps taken to achieve it.
• A cultural or travel experience that broadened your understanding of the world.

Examples of Reflective Essay

Our writers have written numerous examples of reflective essays here are some of them. 

Reflection Essay Example 1

A Poetic Turnaround: How an Online Assignment Platform Reshaped My Perception

Reflection Paper Example 2 

A Day Among Colors and Canvas: Art Exhibition at School 

Reflection Writing Example 3 

Spinning the Semester Around: Witnessing a Friend Succeeding in Their Academic Battles 

Reflective essay writing can polish your being in many ways. By understanding the purpose and importance of reflective essays, as well as mastering the format and selecting meaningful topics, you can transform your writing and unlock the full potential of self-reflection. For additional help, you can avail of our top-of-the-line writing service and confidently pursue your goals, knowing you have the best support for securing impressive grades.

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Best Nature Essay Examples

Nature reflection.

654 words | 3 page(s)

I have been fortunate enough to travel to many of the major global cities in the world such as New York, London, Paris, and Tokyo. One of the common themes shared by these cities is that they remind of the tremendous progress by mankind. But progress rarely comes without costs and in this case, the price of progress has been an increasing distance between mankind and nature. This thought is what inspired me to book an African Safari in Kenya because I wanted to get close to the nature. My trip convinced me that we need to do more to preserve natural habitats because they add invaluable beauty to our small planet.

On first day of the safari trip, we were met were a herd of elephants in a hay-colored grassland. I had never seen such majestic elephants, despite being to many zoos around the world. The sight of elephant alone convinced me that animals do not belong in zoos because no living being reaches optimal physical and mental health in captivity. The elephants came near to our tour car and even extended their trunk to us as if they were saying ‘welcome to our home’. When elephants walked, the earth would tremble. One could say elephants made the land trembled beneath them as dinosaurs once did millions of years ago. Another thing I noticed was that the air was surprisingly clean and energized our bodies as we inhaled it. It was enough to tell me how much we had polluted the air in big cities. Despite all luxuries of life, I had never breathed air this clean and refreshing before even though it was a hot summer day. From time to time, one would get the whiff of the plantation around such as grass and trees.

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On the second day of the tour, our guide led us to the habitat of elephants. Our guide car would pass by the lions and they would not be even the least bit aggressive. Once in a while, one of the lions would roar and it would shake me up. Listening to a roar in person is nothing like listening to it on TV. Even though I knew I am safe in the car, if the roar could tremble me to such an extent, I could only imagine the effect it would have on those animals lions prey on.

The third day was a huge surprise as our guides had set up a lunch table for us beneath a tree in the middle of the jungle. The food was delicious but it tasted even better. I imagined our earliest ancestors who lived in lands like these at ate under these conditions. Eating in the same place enabled me to get in touch with mankind roots. During the lunch, a bunch of deer came by the table because who could resist the smell of delicious food our hosts had prepared for us. The deer were wary of us but the hosts were experienced so they found a way to make the deer feel at ease. This provided me with a valuable opportunity to touch them and just as I had imagined, the feel was akin to touching a goat. I almost shared a quarter of my food with the deer but it was worth the price of getting close to them. As far as the food is concerned, it reminded of Haitian cuisine which shows how similar cultures are around the world. Almost every cultural food I have eaten, it has reminded me of some other culture.

The whole safari experience greatly exceeded my expectations and also made me determined to raise awareness about the damage human beings are doing to natural habitats. Now every time I hear or read about global warming, I cannot help but wonder how it may affect the few remaining natural habitats we are left with whether in Kenya or in Alaska, US.

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Guest Essay

How to Breathe With the Trees

By Margaret Renkl

Ms. Renkl is a contributing Opinion writer who covers flora, fauna, politics and culture in the American South.

Even on a computer screen, Ada Limón, who is serving her second term as poet laureate of the United States, projects such warmth and reassurance that you could almost swear she was sitting beside you, holding your hand. This kind of connection between strangers, human heart to human heart, is so rare as to be startling, especially these days.

April is National Poetry Month, and it strikes me that no one is better positioned than Ms. Limón to convince Americans to leave off their quarrels and worries, at least for a time, and surrender to the language of poetry. That’s as much because of her public presence as because of her public role as the country’s poet in chief. When Ada Limón tells you that poetry will make you feel better, you believe her.

In her nearly weekly travels as poet laureate, Ms. Limón has had a lot of practice delivering this message. “Every time I’m around a group of people, the word that keeps coming up is ‘overwhelmed,’” she said. “It’s so meaningful to lean on poetry right now because it does make you slow down. It does make you breathe.”

A poem is built of rests. Each line break, each stanza break and each caesura represents a pause, and in that pause there is room to take a breath. To ponder. To sit, for once in our lives, with mystery. If we can’t find a way to slow down on our own, to take a breath, poems can teach us how.

But Ms. Limón isn’t merely an ambassador for how poetry can heal us . She also makes a subtle but powerful case for how poetry can heal the earth itself. At this time of crisis, when worry governs our days, she wants us to look up from our screens and consider our own connection to the earth. To remember how to breathe by spending some time with the trees that breathe with us.

In the United States, about half of poets laureate spend their terms developing a signature project that fosters a greater appreciation of poetry. Ms. Limón has two: “ You Are Here: Poetry in the Natural World ,” an anthology of nature poetry that will be released on Tuesday; and “ You Are Here: Poetry in the Parks ,” a series of poetry-centered picnic-table-style installations in seven national parks. Each will be inscribed with the words of a poet associated with that landscape and also with a writing prompt designed to nudge readers to try their own hands at making a poem. These initiatives will be formally introduced on Thursday at the Library of Congress in conjunction with the library’s inaugural Mary Oliver Memorial Event.

Whether sweeping and magnificent or nearly microscopic — a majestic national park vista, say, or an ant colony’s communal effort to save its own inadvertently uncovered eggs — the natural world has always been a catalyst for lyricism. “There’s a reason why people go to these incredible natural landscapes and think, ‘I have no words,’” Ms. Limón said. “And yet the poets, we love to see if we can figure out some words: ‘Let’s see if we can name that kind of wonder, that kind of awe.’”

The connection between the beauty of the world and the beauty of the language is more crucial now than it has ever been. In its intimacy, its revelation not just of nature but also of the perceiving self, nature poems offer one of the few paths we have to consider the risks to the natural world in a way that is free of partisan rancor.

Those risks are foremost in Ms. Limón’s mind. In considering what her signature project as poet laureate would be, the thought that came to her was both small and impossibly huge: “I just want us all to write poems and save the planet,” she writes in the introduction to “You Are Here.”

“We all have nature poems within us — every single one of us,” Ms. Limón said when I asked her about this statement. “I wanted to have a book that not only allowed us to think of many different ways that nature poems can exist and move in the world, but also give people permission to write their own nature poems and think about it in a different way.”

“You Are Here” is an anthology of nature poems by 50 of the most accomplished poets working today, including the PEN/Voelcker Award winner Rigoberto González, the former U.S. poet laureate Joy Harjo, the Pulitzer Prize winner Diane Seuss and the Kingsley Tufts Award winner Patricia Smith, among many others who have won national awards for their work. “I just asked for these original poems, like, ‘Will you make this poem that speaks back to the natural world, whatever that means to you?’” Ms. Limón said.

The poems she got in response represent a great diversity of poetic voices and forms, and also a diversity of natural landscapes. If your idea of nature poetry is, as Ms. Limón said only half-jokingly, “a young gentleman walking to a mountain and having an epiphany,” this anthology will put that notion to rest.

Whoever you are, you will find yourself and your own world in the expansiveness of this collection. Even in the specificity of each poet’s own inimitable experience, you will find your own voice and your own perceiving self, for the natural world includes us and enfolds us all. Nature can be found on a mountain, yes, but it can also be found on a city stoop. Or in a drainage ditch. Or in the sky above a prison yard. Wherever we are, that is where the natural world is, too. It is there. We just have to notice it.

Writing a poem might seem like the least practical way imaginable to address melting glaciers, bleaching coral, drought, pollution and the like, never mind the overarching catastrophes of climate change and mass extinction. What can language do to save us now? What can something so small as a poem possibly do to save us now?

The answer lies in poetry’s great intimacy, its invitation to breathe together. We read a poem, and we take a breath each time the poet takes a breath. We read a nature poem, and we take a breath with the trees. When the trees — and the birds and the clouds and the ants and even the bats and the rat snakes — become a part of us, too, maybe that’s when we will finally begin to care enough to save them.

Margaret Renkl , a contributing Opinion writer, is the author of the books “ The Comfort of Crows: A Backyard Year, ” “ Graceland, at Last ” and “ Late Migrations .”

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

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Rise up in ... reflection!

Holding to our true nature as God’s children, reflecting His harmony, empowers us to rebel against illness and experience healing.

• By Elizabeth Mata

April 1, 2024

These days it seems like across the globe human thought is in a constant stir and uprising – a rebellion, if you will. Throughout the ages there have been individuals who have bravely and selflessly fought some injustice, inspired by a deep love for humanity and for what is right.

Above all, there’s Christ Jesus, who revolutionized the world through his teachings and healings. He certainly rose up against whatever would limit and imprison human thought, including hypocrisy and oppression. Mary Baker Eddy , the discoverer of Christian Science and a follower of Jesus’ teachings, wrote in her main book, “Science and Health with Key to the Scriptures,” “Jesus acted boldly, against the accredited evidence of the senses, against Pharisaical creeds and practices, and he refuted all opponents with his healing power” ( p. 18 ).

Here it is clear that Jesus’ “rebellion” wasn’t personal or fueled by anger. He was not waging war against others or in danger of losing his stability and peace even as he “acted boldly.” He was bringing the healing power of God, good, to counter “the accredited evidence of the senses.”

One way Jesus characterized and summed up his actions was, as God’s Word translation renders it, “I can guarantee this truth: The Son cannot do anything on his own. He can do only what he sees the Father doing. Indeed, the Son does exactly what the Father does” ( John 5:19 ).

Jesus didn’t hesitate to speak – or prove – the truth about his oneness with God, the only true creator, the Father of us all. It was this heavenly Father, divine Love and Spirit, that guided and governed Jesus’ every word and action. This enabled Jesus to take a spiritual stand against all false concepts about God and man – that is, perceptions that are based on the material senses and that would deny the authority of God.

This would include the notion that we are mortals vulnerable to problems. Jesus’ healings showed that divine Spirit is supreme and created each of us in God’s image, spiritual and flawless. His understanding of spiritual reality destroyed the pretense to power, presence, and reality of these false concepts – and healing resulted.

The teachings of Christian Science bring out with shining light that it was Christ – Jesus’ “divine nature, the godliness which animated him” ( Science and Health, p. 26 ) – that impelled all the Master did. Christ reveals that everyone’s true nature is the reflection of God, in perfect spiritual quality and expression. The essence of reflection means absolute obedience to God, or reflecting His nature with complete consistency. Through prayer in Christian Science, we can grasp that spiritual law holds us in perfect obedience to God’s goodness – and increasingly bear witness to this unwavering truth through healing and transformed lives.

There’s a chapter titled “Fruitage” at the end of Science and Health, which is filled with testimonies from individuals who were healed of all kinds of diseases and other evils simply through reading the book. One individual shared how she was healed of yellow fever, poor vision, and severe headaches, among other physical problems. She wrote, “Mrs. Eddy has made Scripture reading a never-failing well of comfort to me. By her interpretation ‘the way of the Lord’ is made straight to me and mine. It aids us in our daily overcoming of the tyranny of the flesh and its rebellion against the blessed leading of Christ, Truth” ( p. 666 ).

This “blessed leading” is at hand in this moment (and every moment) to gently guide our every thought and action to safety and serenity. For instance, claiming our true nature as reflecting divine Love brings out the powerlessness and illegitimacy of aggressive behavior and naturally brings greater compassion, tenderness, and gentleness to our actions. Holding to the truth that we all express the integrity and purity of divine Spirit reveals illness, dishonesty, and deceit to be without foundation.

So when we’re confronted with something that runs counter to our God-given freedom, we can “rise up in reflection,” so to speak, to subdue and destroy that element of carnal mind thinking – which the Bible calls “enmity against God” ( Romans 8:7 ). Then we see that this carnal mind is not a powerful force we must bitterly fight back against, but wholly without substance. And we are empowered to prove that, step by step, and experience healing.

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• Open access
• Published: 19 March 2024

Heterogeneity and predictors of the effects of AI assistance on radiologists

• Feiyang Yu   ORCID: orcid.org/0000-0001-6748-1070 1 , 2   na1 ,
• Alex Moehring   ORCID: orcid.org/0000-0001-6822-6354 3   na1 ,
• Oishi Banerjee 1 ,
• Tobias Salz 4   na2 ,
• Nikhil Agarwal 4   na2 &
• Pranav Rajpurkar   ORCID: orcid.org/0000-0002-8030-3727 1   na2  

Nature Medicine volume  30 ,  pages 837–849 ( 2024 ) Cite this article

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• Machine learning
• Radiography

The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician–AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice.

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The integration of artificial intelligence (AI) into medical image interpretation has shown great potential for improving diagnostic accuracy and efficiency 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 . Collaborative setups, where AI systems assist clinicians in decision-making, have emerged as practical approaches to harness the benefits of AI while leveraging clinician expertise 10 , 11 , 12 , 13 . However, to optimize the implementation of AI in clinical practice, it is crucial to have a comprehensive understanding of the heterogeneity—the diverse and individualized effects—of AI assistance on clinicians. Clinicians possess varying levels of expertise, experience and decision-making styles, and ensuring that AI support accommodates this heterogeneity is essential for targeted implementation and maximizing the positive impact on patient care.

Previous studies on clinician–AI collaboration predominantly focused on analyzing groups of clinicians as a whole, overlooking the variations in how AI affects individual clinicians 14 , 15 , 16 , 17 , 18 , 19 , 20 . Although some studies explored the heterogeneity of AI effects based on factors such as a radiologist’s seniority 21 , 22 , 23 , task expertise 24 and experience level 25 , 26 , 27 , 28 , these studies have certain limitations. They often measure changes in predictions rather than changes in prediction accuracy, and they tend to neglect potential predictors, such as experience with AI tools. Additionally, although some research considered indirect measures of diagnostic skill, such as years of experience, there remains a limited understanding of whether direct measures of clinicians’ diagnostic skill can accurately predict the effects of AI assistance. Therefore, conducting a comprehensive investigation into the heterogeneous effects of AI is crucial for determining which clinicians should receive AI assistance in real-world healthcare settings.

In the present study, we investigated the predictors of heterogeneous treatment effects of AI assistance in radiology, where treatment effect refers to the change in diagnostic performance of radiologists from without to with AI assistance. To achieve this, we examined a large-scale diagnostic study that measured the performance of 140 radiologists with and without AI assistance on 15 chest X-ray diagnosis tasks. Participating radiologists received onboarding training on the assistive AI system before starting the experiment and were shown example AI predictions from the same AI model used in the experiment, which would help them calibrate their interpretation of AI predictions and inform their incorporation of AI. Our analysis focuses on the influence of experience-based predictors, direct measures of diagnostic skill and AI error on the outcome of treatment effects and examines this influence in terms of both calibration performance and discrimination performance. We found substantial heterogeneity in the treatment effects of AI assistance among radiologists. Moreover, our findings reveal that experience-based characteristics and direct measures of diagnostic skill prove inadequate in predicting the treatment effect of AI assistance on radiologists. Additionally, we highlight the influential role of AI error on the treatment effect. Lower absolute AI error leads to a greater treatment effect on all pathologies aggregated and on half of the individual pathologies investigated, and the direction of AI error also impacts the treatment effect outcome. By uncovering the heterogeneity of AI effects and identifying predictors of the treatment effect, our study offers valuable insights for the targeted implementation of AI assistance in clinical practice. Comprehending the factors contributing to the heterogeneity of AI effects is vital for the development of tailored strategies to optimize clinician–AI collaboration, to guide resource allocation and training efforts and to foster trust and acceptance among clinicians.

Heterogeneous treatment effects of AI assistance

We analyzed data collected using a diagnostic study involving 140 radiologists, 324 patient cases and 15 pathologies with corresponding AI predictions from two study designs: one with repeated measurements on the same case and one without 29 . The non-repeated-measure design involved 107 radiologists who each reviewed a total of 60 patient cases, with 30 cases assessed without AI assistance and 30 cases assessed with AI assistance. For each set of 30 patient cases, radiologists examined half without clinical histories and half with clinical histories. The repeated-measure design included 33 radiologists who evaluated 60 patient cases under four conditions: with AI assistance and clinical histories, with AI assistance without clinical histories, without AI assistance with clinical histories and without either AI assistance or clinical histories. In our analysis, we combined data from the clinical history conditions and investigated the heterogeneous treatment effect of AI assistance.

In this study, calibration performance was measured by absolute error. Absolute error was defined as the absolute difference between the radiologist-predicted probability and the ground truth probability on a 0–100 scale. Treatment effect was defined as the improvement in absolute error, specifically the difference between the absolute error of a radiologist when unassisted by AI (unassisted error) and the absolute error when assisted by AI (assisted error), unless otherwise specified. Absolute error was the primary metric of analysis; thus, references to performance and treatment effects are based on this metric by default. Discrimination performance was measured by area under the receiver operating characteristic (ROC) curve, where the ground truth labels were computed by thresholding the continuous ground truth probabilities at 50. Treatment effect on AUROC was defined as the improvement in AUROC, specifically the difference between the AUROC of a radiologist when assisted by AI (assisted AUROC) and the AUROC when unassisted by AI (unassisted AUROC).

Our findings revealed substantial heterogeneity in the treatment effects of AI assistance among different radiologists (Extended Data Fig. 1 and Supplementary Table 1 ). When measuring AI’s treatment effect as the improvement in absolute error across all pathologies, we observed a range of treatment effects from −1.295 to 1.440 (interquartile range (IQR), 0.797). Notably, for high-prevalence pathology labels (pathology labels with prevalence greater than 10% in the dataset), the largest range of treatment effects extended from −8.914 to 5.563 (IQR, 3.245) for detecting whether chest X-rays are abnormal. In comparison, when examining the distribution of radiologists’ unassisted error (Extended Data Fig. 2 ), we observed an average range of unassisted error from 6.083 to 14.175 (IQR, 1.951) across pathologies. The significant heterogeneity in treatment effects indicates that the impact of treatment effects ranging from −1.295 to 1.440 (IQR, 0.797) could substantially influence the absolute performance and relative performance of radiologists compared to their peers. Furthermore, the heterogeneity in treatment effects on high-prevalence pathology labels remained substantial when compared to radiologists’ unassisted error.

Additionally, we found substantial heterogeneity in treatment effects on sensitivity and specificity. The range of treatment effects on radiologists’ sensitivity and specificity averaged from 1.9% to 11.8% (IQR, 1.9%) and from −4.0% to 3.1% (IQR, 1.6%), respectively, across all pathologies (Extended Data Fig. 3 ). In comparison, the range of unassisted sensitivities spanned from 20.0% to 92.7% (IQR, 15.3%), whereas the range of unassisted specificities ranged from 81.5% to 99.2% (IQR, 4.0%). These findings indicate substantial heterogeneity in treatment effects on sensitivity and specificity, which aligns with observations regarding absolute error.

Experience-based characteristics as predictors

We studied whether experience-based radiologist characteristics could function as potential predictors of treatment effect. Specifically, we examined three characteristics: years of experience (explored in previous work 24 , 25 , 26 , 27 , 28 ), subspecialty in thoracic radiology (explored in previous work 24 ) and experience with AI tools (an understudied characteristic that approximates the ability to use AI). These characteristics were collected through a post-experiment survey completed by 136 radiologists.

To establish a benchmark, we divided the same 136 radiologists into binary subgroups using an oracle predictor and median treatment effect as the cutoff. We computed the subgroup treatment effects on all pathologies aggregated (Fig. 1a ) and high-prevalence pathology labels (Extended Data Fig. 4a ) while shrinking the individual radiologist treatment effects using the empirical Bayes method 30 to ameliorate overestimation of heterogeneity due to measurement error in radiologist performance. We observed a statistically significant difference of −0.828 (232%) between the two subgroups on all pathologies aggregated ( P  < 0.001; Supplementary Table 2 ) and on each high-prevalence pathology label (Benjamini–Hochberg-adjusted P  < 0.001), indicating that radiologists with a higher than median treatment effect had a significantly higher treatment effect than those with a treatment effect lower than or equal to the median. This finding suggests a significant heterogeneity between radiologists and shows the extent of heterogeneity that an ideal predictor would have been able to discern.

a , Heterogeneous treatment effects of subgroups of radiologists on all pathologies aggregated. The treatment effects were shrunk toward the mean using the empirical Bayes method. Statistically significant heterogeneity was observed between subgroups ( P  = 3.50 × 10 −34 ), where radiologists with a higher than median treatment effect had a significantly higher treatment effect of 0.472 (95% CI: 0.403 to 0.541) than those with a treatment effect lower than or equal to the median of −0.357 (95% CI: −0.429 to 0.284). A two-sided, unpaired t -test between the two subgroups of treatment effects was conducted. Testing for all pathologies aggregated did not constitute multiple hypothesis testing. The error bars show 95% CIs. b , Treatment effects on all pathologies aggregated of subgroups of radiologists based on combined characteristics of years of experience, subspecialty in thoracic radiology and experience with AI tools. No statistically significant difference was observed between the lower predicted treatment effect subgroup, 0.091 (95% CI: −0.231 to 0.413), and the higher predicted treatment effect subgroup, 0.070 (95% CI: −0.243 to 0.383) ( P  > 0.05). The Wald test was used to test regression coefficients that estimate treatment effects against the null hypothesis of joint equality among treatment effects of different subgroups. Details of the statistical models are available in the Methods . There are 136 radiologists with available survey data on the three characteristics. The error bars show 95% CIs. NS indicates no statistical significance ( P  > 0.05). c , Same subfigures as in a and b based on years of experience (left), subspecialty in thoracic radiology (middle) and experience with AI tools (right), respectively. The same statistical test as in b was used. d , Same subfigure as in b for AUROC on all pathologies aggregated. The same statistical test as in b was used. e , Same subfigures as in d based on years of experience (left), subspecialty in thoracic radiology (middle) and experience with AI tools (right), respectively. The same statistical test as in b was used.

To understand the predictive power of experience-based radiologist characteristics together, we built a combined characteristics linear regression model that used binary variables for years of experience (whether the radiologist had less than or equal to the median 6 years of experience), subspecialty in thoracic radiology (whether the radiologist specialized in thoracic radiology) and experience with AI tools (whether the radiologist had experience with AI tools) as independent variables and an intercept term to predict the mean treatment effect of each radiologist. We found that the combined characteristics model was a poor predictor of treatment effect on all pathologies aggregated (Fig. 1b ) and individual pathologies (Extended Data Fig. 4b ). No statistically significant difference was observed in treatment effect between subgroups on all pathologies aggregated ( P  > 0.05; Supplementary Table 3 ) or on individual pathologies ( P  > 0.05, Benjamini–Hochberg-adjusted P  > 0.05).

To assess the impact of these characteristics as individual predictors on treatment effects, we divided the radiologists into binary subgroups based on the median value of each predictor. Subsequently, we conducted tests to identify any significant differences in treatment effects between the subgroups created based on the predictor values. When used individually, each of the experience-based characteristics was found to be a poor predictor of treatment effect: no statistically significant difference was observed in treatment effect between subgroups on all pathologies aggregated ( P  > 0.05; Fig. 1c and Supplementary Tables 4 – 6 ). Except for the AI experience predictor on edema ( P  = 0.009, Benjamini–Hochberg-adjusted P  > 0.05), there was also no statistically significant difference between subgroups split based on individual characteristics for any pathology ( P  > 0.05, Benjamini–Hochberg-adjusted P  > 0.05; Extended Data Fig. 4c ).

In addition to absolute error and calibration performance, we conducted the same analyses for AUROC as the metric and discrimination performance. We found that the combined characteristics model was again a poor predictor of treatment effect on AUROC on all pathologies aggregated (Fig. 1d ) and individual pathologies (Extended Data Fig. 5a ). No statistically significant difference was observed in treatment effect between subgroups on all pathologies aggregated ( P  > 0.05; Supplementary Table 27 ) or on individual pathologies ( P  > 0.05, Benjamini–Hochberg-adjusted P  > 0.05). Each of the individual experience-based characteristics was also found to be a poor predictor of treatment effect on AUROC: no statistically significant difference was observed in treatment effect between subgroups on all pathologies aggregated ( P  > 0.05; Fig. 1e and Supplementary Tables 28 – 30 ). Except for the AI experience predictor on airspace opacity ( P  = 0.045, Benjamini–Hochberg-adjusted P  > 0.05), there was also no statistically significant difference between subgroups split based on individual characteristics for any pathology ( P  > 0.05, Benjamini–Hochberg-adjusted P  > 0.05; Extended Data Fig. 5b ).

Unassisted performance as a predictor of treatment effect

In addition to experience-based radiologist characteristics, we investigated whether the diagnostic skill of radiologists, as measured by their unassisted error on the specific dataset and task, could serve as a viable predictor of treatment effect. We constructed a linear regression model, where the independent variable was the unassisted error and the dependent variable was the treatment effect, accounting for attenuation bias. We employed a split sampling approach, using distinct sets of patient cases to calculate the unassisted error and treatment effect.

We observed that the regression coefficient on unassisted error was positive but not statistically significant on all pathologies aggregated ( P  > 0.05; Fig. 2a , left, and Supplementary Table 7 ). This finding suggests that unassisted error is an inadequate predictor of treatment effect. Among the individual pathologies, the regression coefficient was significant on abnormal ( P  = 0.005), lesion ( P  = 0.003) and atelectasis ( P  = 0.016) without correcting for multiple hypothesis testing. However, the regression coefficient was not significant on all individual pathologies after correction (Fig. 2b ). This suggests that unassisted error also poorly predicts treatment effect at the individual pathology level.

a , Unassisted error is a poor predictor of treatment effect on all pathologies aggregated (left). Without split sampling, there is a hallucinated association between treatment effect and unassisted error (right). The binscatter plots contain five evenly spaced bins containing 5,190 data points in total. The gray regression line is fitted on the raw data. The five bins are presented as −0.254 (95% CI: −0.701 to 0.211), −0.205 (95% CI: −0.431 to 0.017), 0.590 (95% CI: 0.291 to 0.878), 0.301 (95% CI: −0.487 to 1.093) and −0.419 (95% CI: −2.178 to 1.125) (left) and −1.148 (95% CI: −1.473 to −0.842), 0.078 (95% CI: −0.165 to 0.313), 0.661 (95% CI: 0.327 to 0.971), 0.979 (95% CI: 0.269 to 1.772) and 0.409 (95% CI: −0.982 to 1.919) (right). The blue dotted regression line is the final regression output after adjusting for attenuation bias. The translucent band around the blue regression line represents the 95% CI. * and ** indicate statistically significant difference from zero at a significance level of 0.05 and 0.01, respectively. NS indicates no statistical significance ( P  > 0.05). b , Unassisted error is a poor predictor of treatment effect on each individual pathology. The binscatter plots are designed in the same way as those in a . The significance of the slope coefficients is determined through the Benjamini–Hochberg procedure, respectively, to correct for multiple hypothesis testing (15 individual pathologies) at a false discovery rate of 0.05 (*) and 0.01 (**). NS indicates no statistical significance (Benjamini–Hochberg-adjusted P  > 0.05). c , Same subfigures as in a for AUROC on all pathologies aggregated. Unassisted AUROC is a poor predictor of treatment effect on AUROC on all pathologies aggregated (left). Without split sampling, there is a hallucinated association between treatment effect on AUROC and unassisted AUROC (right). The five bins are presented as −0.073 (95% CI: −0.196 to 0.044), 0.057 (95% CI: −0.014 to 0.157), 0.065 (95% CI: 0.036 to 0.095), 0.022 (95% CI: 0.006 to 0.038) and 0.033 (95% CI: 0.016 to 0.052) (left) and 0.119 (95% CI: 0.058 to 0.187), 0.074 (95% CI: 0.034 to 0.111), 0.065 (95% CI: 0.050 to 0.079), 0.018 (95% CI: 0.007 to 0.028) and 0.001 (95% CI: −0.011 to 0.012) (right). w/o, without.

We repeated the same analysis for AUROC and found that the regression coefficient on unassisted AUROC was negative and not statistically significant on all pathologies aggregated ( P  > 0.05; Fig. 2c , left, and Supplementary Table 31 ). This suggests that unassisted AUROC is also a poor predictor of treatment effect in terms of discrimination performance.

Preventing reversion to the mean using split sampling

We found that the use of split sampling was crucial in our analysis. This method ensured that unassisted error and treatment effect were calculated using separate sets of patient cases, preventing the spurious correlation caused by reversion to the mean 31 —the statistical phenomenon where subsamples that deviate significantly from the mean are more likely to converge toward the mean in subsequent subsamples. In the context of this study, if a radiologist produces a lower-than-average quality diagnostic assessment on an unassisted case by chance, this same radiologist is very likely to produce a better diagnostic assessment on the same case when assisted, resulting in a positive treatment effect; conversely, if a radiologist produces a good diagnosis on a case unassisted by chance, the radiologist is likely to produce a worse diagnosis on the same case assisted, resulting in a negative treatment effect. This phenomenon, therefore, falsely creates a positive correlation between unassisted error and treatment effect (unassisted error minus assisted error). Split sampling prevents reversion to the mean by using disjoint patient cases to compute unassisted error and treatment effect for each data point in the linear regression model.

To demonstrate the importance of split sampling, we constructed a naive model that involved using all available patient cases to compute the unassisted error (independent variable) and treatment effect (dependent variable). When we applied the naive model, we observed a substantial hallucinated correlation between unassisted error and treatment effect (Fig. 2a , right). On all pathologies aggregated, the hallucinated regression coefficient was 0.357 (adjusted for attenuation bias resulting from measurement error in radiologist performance) and 0.309 (unadjusted), both statistically significant ( P  < 0.001; Supplementary Table 8 ). Similar hallucinated correlations were observed across individual pathologies (Benjamini–Hochberg-adjusted P  < 0.05). These findings underscore the necessity of split sampling to mitigate the effects of reversion to the mean.

With AUROC, we observed negative hallucinated correlations between unassisted AUROC and treatment effect on AUROC on all pathologies aggregated ( P  < 0.001; Fig. 2c , right, and Supplementary Table 32 ) and individual pathologies (Benjamini–Hochberg-adjusted P  < 0.001). Because AUROC is an aggregate metric over a set of patient cases, the effects of reversion to the mean on AUROC cannot be untangled at the case level. However, the dramatic hallucination of correlations again emphasizes the necessity of split sampling.

Higher-performing radiologists are still higher performing

Considering the inadequate predictive power of unassisted error on treatment effect, we hypothesized that the relative performance of radiologists with and without AI assistance would remain largely consistent. To test this hypothesis, we constructed a linear regression model that regresses from unassisted error, the independent variable, and an intercept term to assisted error, the dependent variable. We adjusted for attenuation bias on the independent variable. To again avoid reversion to the mean, we adopted a split sampling approach in which we used separate sets of patient cases to compute unassisted error and assisted error for each radiologist.

The results revealed that the regression coefficient on unassisted error was significantly different from zero when considering all pathologies aggregated ( P  < 0.001; Fig. 3a and Supplementary Table 9 ). Similarly, the regression coefficient was significant on most individual pathologies (Benjamini–Hochberg-adjusted P  < 0.05), except for atelectasis, pneumothorax and shoulder fracture (Benjamini–Hochberg-adjusted P  > 0.05; Fig. 3b ). We similarly constructed a linear regression model regressing from unassisted AUROC and an intercept term to assisted AUROC. The regression coefficient on unassisted AUROC was again significant on all pathologies aggregated ( P  < 0.001; Fig. 3c and Supplementary Table 33 ), whereas the coefficient was insignificant on abnormal ( P  > 0.05). Together, these findings indicate that unassisted error serves as a strong predictor of assisted error in most cases.

a , Higher-performing radiologists are still higher performing after receiving AI assistance on all pathologies aggregated. The binscatter plots contain five evenly spaced bins containing 5,190 data points in total. The gray regression line is fitted on the raw data. The five bins are presented as 7.950 (95% CI: 7.579 to 8.339), 8.569 (95% CI: 8.342 to 8.827), 10.197 (95% CI: 9.870 to 10.531), 12.013 (95% CI: 11.245 to 12.857) and 14.870 (95% CI: 13.418 to 16.358). The red dotted regression line is the final regression output after adjusting for attenuation bias. The translucent band around the red regression line represents the 95% CI. * and ** indicate statistically significant difference from zero at a significance level of 0.05 and 0.01, respectively. b , Higher-performing radiologists are still higher performing after receiving AI assistance on each individual pathology except for atelectasis, pneumothorax and shoulder fracture, where the regression coefficients for the slope are not statistically significantly different from zero. The binscatter plots are designed in the same way as those in a . The five bins for abnormal are presented as 22.712 (95% CI: 21.050 to 24.323), 28.470 (95% CI: 27.411 to 29.528), 33.359 (95% CI: 32.298 to 34.497), 41.729 (95% CI: 40.406 to 43.045) and 43.904 (95% CI: 39.488 to 48.428). The four bins for airspace opacity are presented as 15.590 (95% CI: 14.809 to 16.401), 17.940 (95% CI: 17.198 to 18.718), 22.414 (95% CI: 20.828 to 24.185) and 41.076 (95% CI: 33.894 to 48.061). The five bins for atelectasis are presented as 11.550 (95% CI: 10.558 to 12.505), 10.638 (95% CI: 10.030 to 11.221), 11.366 (95% CI: 10.070 to 12.691), 9.777 (95% CI: 7.909 to 11.676) and 12.262 (95% CI: 9.144 to 15.681). The five bins for bacterial/lobar pneumonia are presented as 4.191 (95% CI: 3.645 to 4.745), 4.443 (95% CI: 4.069 to 4.842), 5.474 (95% CI: 4.499 to 6.499), 6.619 (95% CI: 4.202 to 9.317) and 9.861 (95% CI: 7.620 to 12.169). The five bins for cardiomediastinal abnormality are presented as 14.061 (95% CI: 13.295 to 14.817), 16.268 (95% CI: 15.609 to 16.867), 21.241 (95% CI: 19.556 to 22.881), 24.820 (95% CI: 20.349 to 29.464) and 34.358 (95% CI: 30.263 to 38.553). The five bins for cardiomegaly are presented as 10.169 (95% CI: 9.615 to 10.727), 14.611 (95% CI: 13.737 to 15.516), 19.483 (95% CI: 16.558 to 22.551), 39.736 (95% CI: 32.091 to 46.737) and 26.732 (95% CI: 17.879 to 35.100). The five bins for consolidation are presented as 5.553 (95% CI: 4.727 to 6.387), 6.230 (95% CI: 5.826 to 6.604), 7.219 (95% CI: 6.447 to 8.093), 9.631 (95% CI: 7.575 to 11.707) and 11.915 (95% CI: 9.683 to 14.186). The five bins for edema are presented as 7.761 (95% CI: 7.127 to 8.419), 12.777 (95% CI: 11.909 to 13.616), 18.254 (95% CI: 16.937 to 19.523), 19.268 (95% CI: 16.246 to 22.671) and 39.059 (95% CI: 27.886 to 49.837). The four bins for lesion are presented as 3.185 (95% CI: 2.973 to 3.386), 4.507 (95% CI: 3.856 to 5.170), 2.738 (95% CI: 1.476 to 4.203) and 22.835 (95% CI: 13.974 to 32.555). The five bins for pleural effusion are presented as 3.404 (95% CI: 2.794 to 4.077), 3.473 (95% CI: 2.950 to 4.013), 4.401 (95% CI: 3.644 to 5.173), 5.940 (95% CI: 4.794 to 7.119) and 6.073 (95% CI: 4.431 to 7.915). The five bins for pleural other are presented as 0.410 (95% CI: 0.348 to 0.473), 0.729 (95% CI: 0.529 to 0.938), 1.202 (95% CI: 0.626 to 2.120), 1.668 (95% CI: 0.549 to 3.153) and 6.883 (95% CI: 3.940 to 10.317). The five bins for pneumothorax are presented as 0.689 (95% CI: 0.534 to 0.880), 0.675 (95% CI: 0.493 to 0.891), 1.235 (95% CI: 0.697 to 2.004), 1.425 (95% CI: 0.166 to 3.263) and 1.345 (95% CI: 0.712 to 2.059). The five bins for rib fracture are presented as 2.614 (95% CI: 2.246 to 3.019), 3.405 (95% CI: 2.902 to 3.942), 3.357 (95% CI: 2.699 to 4.112), 2.707 (95% CI: 1.309 to 4.579) and 5.386 (95% CI: 3.032 to 8.266). The five bins for shoulder fracture are presented as 0.594 (95% CI: 0.434 to 0.768), 1.093 (95% CI: 0.582 to 1.765), 0.712 (95% CI: 0.477 to 0.968), 0.171 (95% CI: 0.012 to 0.462) and 0.050 (95% CI: 0.000 to 0.118). The five bins for support device hardware are presented as 9.389 (95% CI: 8.799 to 10.016), 12.652 (95% CI: 11.702 to 13.586), 14.477 (95% CI: 11.126 to 18.418), 36.781 (95% CI: 30.346 to 43.310) and 45.214 (95% CI: 35.945 to 54.983). The significance of the slope coefficients is determined through the Benjamini–Hochberg procedure, respectively, to account for multiple hypothesis testing (15 individual pathologies) at a false discovery rate of 0.05 (*) and 0.01 (**). NS indicates no statistical significance (Benjamini–Hochberg-adjusted P  > 0.05). c , Same subfigure as in a for AUROC on all pathologies aggregated. Higher-performing radiologists as measured by AUROC are still higher performing after receiving AI assistance on all pathologies aggregated. The five bins are presented as 0.770 (95% CI: 0.697 to 0.834), 0.814 (95% CI: 0.775 to 0.855), 0.874 (95% CI: 0.859 to 0.888), 0.889 (95% CI: 0.877 to 0.900) and 0.924 (95% CI: 0.911 to 0.936).

AI error as a predictor of treatment effect

We investigated whether higher-quality AI assistance led to better treatment effects on average across radiologists and cases. We computed the treatment effects of AI when the absolute error of AI-predicted probabilities fell into five separate ranges, and we tested for heterogeneity among AI error ranges by testing the joint null hypothesis of equal treatment effects across bins. We found that different AI error ranges resulted in statistically significant differences in treatment effect on all pathologies aggregated ( P  < 0.001; Fig. 4a and Supplementary Table 10 ). More accurate AI predictions led to higher treatment effects: AI assistance with absolute error under 20 resulted in a treatment effect of 0.679 (95% confidence interval (CI): 0.492 to 0.865, n  = 176,130; Supplementary Table 11 ), whereas AI assistance with absolute error above 80 resulted in a treatment effect of −16.845 (95% CI: −24.288 to −9.403, n  = 371). No singular trend was observed across individual pathologies. On abnormal, airspace opacity, bacterial/lobar pneumonia, cardiomediastinal abnormality, cardiomegaly, consolidation, pleural effusion, pleural other, pneumothorax and support device hardware, different AI ranges resulted in statistically significant differences in treatment effect (Benjamini–Hochberg-adjusted P  < 0.05 for support device hardware, Benjamini–Hochberg-adjusted P  < 0.01 for all others). More accurate AI predictions led to better treatment effects on abnormal, airspace opacity, cardiomediastinal abnormality, cardiomegaly, pleural effusion, pleural other, pneumothorax and support device hardware; the reverse trend held for bacterial/lobar pneumonia and consolidation, whereas the trend was unclear for the remaining pathologies (Fig. 4b ).

a , AI has greater treatment effects on radiologists when the AI assistance has lower error on all pathologies aggregated. The five bins are presented as 0.679 (95% CI: 0.492 to 0.865), −1.509 (95% CI: −2.267 to −0.750), −3.556 (95% CI: −4.878 to −2.235), −6.569 (95% CI: −8.764 to −4.374) and −16.845 (95% CI: −24.288 to −9.403). The error bars show 95% CIs. The blue lines show the overall treatment effect across AI error. * and ** indicate statistically significant difference among subgroups of different AI error through a joint equality test at a significance level of 0.05 and 0.01, respectively. b , AI has greater treatment effects on radiologists when the AI assistance has lower error on abnormal, airspace opacity, cardiomediastinal abnormality, cardiomegaly, pleural effusion, pleural other, pneumothorax and support device hardware. The reverse trend holds for bacterial/lobar pneumonia and consolidation. The bar plots are designed in the same way as those in a . The significance of the subgroup joint test is determined through the Benjamini–Hochberg procedure, respectively, to correct for multiple hypothesis testing (15 individual pathologies) at a false discovery rate of 0.05 (*) and 0.01 (**). NS indicates no statistical significance (Benjamini–Hochberg-adjusted P  > 0.05). c , AI has greater treatment effects on AUROC on radiologists when the AI assistance has an absolute error in the range [20, 100], whereas the trend is unclear with absolute error in the range [0, 20].

We conducted the same analysis using AUROC, where we similarly separated predictions into five bins based on the absolute error of the AI-predicted probabilities based on binary ground truth labels and computed the treatment effect on AUROC over all predictions in each bin. We found that different AI error ranges again resulted in statistically significant differences in treatment effect on AUROC on all pathologies aggregated ( P  < 0.001; Fig. 4c and Supplementary Table 34 ). The overall trend was unclear: more accurate AI predictions led to higher treatment effects on AUROC when the AI assistance had an absolute error in the range [20, 100], but treatment effects in the range [0, 20] were smaller than those in the range [20, 60]. For individual pathologies, the AUROC analysis was numerically feasible only for airspace opacity, and the trend was unclear.

These findings suggest that AI error could be a predictor of treatment effect, but the statistical significance and direction of the relationship could differ across pathologies and metrics.

AI that underestimates probabilities leads to better effect

We subsequently examined the impact of the direction of AI error on the resulting treatment effect. We computed the treatment effects of AI across 10 different ranges of signed error, which represents the difference between AI-predicted probabilities and the corresponding ground truth probabilities. Heterogeneity among these AI error ranges was tested using a joint equality hypothesis.

We found that different ranges of AI signed error resulted in statistically significant differences in treatment effect on all pathologies aggregated ( P  < 0.001; Supplementary Tables 12 and 13 ). We observed that AI predictions with negative errors, indicating underestimation of probabilities by the AI, led to better treatment effects compared to predictions with the same magnitude of positive errors, indicating overestimation of probabilities by the AI (Fig. 5a ).

a , AI has greater treatment effects on radiologists when the AI assistance underestimates probabilities, rather than overestimates probabilities, given the same absolute error on all pathologies aggregated. The nine bins are presented as −3.560 (95% CI: −9.472 to 2.353), 2.089 (95% CI: 0.334 to 3.844), 2.698 (95% CI: 1.497 to 3.899), 2.483 (95% CI: 1.897 to 3.070), 0.398 (95% CI: 0.235 to 0.561), −2.933 (95% CI: −3.787 to −2.079), −5.018 (95% CI: −6.516 to −3.519), −6.968 (95% CI: −9.346 to −4.591) and −16.845 (95% CI: −24.288 to −9.403). The error bars show 95% CIs. The blue bars show the overall treatment effect across AI error. * and ** indicate statistically significant difference among subgroups of different AI signed error through a joint equality test at a significance level of 0.05 and 0.01, respectively. b , AI has greater treatment effects on radiologists when the AI assistance underestimates probabilities, rather than overestimates probabilities, given the same absolute error on airspace opacity, atelectasis, cardiomegaly, consolidation and lesion. The bar plots are designed in the same way as those in a . The significance of the subgroup joint test is determined through the Benjamini–Hochberg procedure, respectively, to correct for multiple hypothesis testing (15 individual pathologies) at a false discovery rate of 0.05 (*) and 0.01 (**). NS indicates no statistical significance (Benjamini–Hochberg-adjusted P  > 0.05).

No singular trend was observed across individual pathologies. For eight pathologies (abnormal, airspace opacity, atelectasis, cardiomegaly, consolidation, lesion, pleural other and rib fracture), different AI error ranges showed statistically significant differences in treatment effect (Benjamini–Hochberg-adjusted P  < 0.01). Among these pathologies, AI predictions that underestimated probabilities led to better treatment effects on airspace opacity, atelectasis, cardiomegaly, consolidation and lesion, whereas the trend was unclear for the remaining pathologies (Fig. 5b ).

The same analysis could not be repeated for AUROC, because there were not both cases with the pathology present and cases with the pathology not present in each non-empty bin on all pathologies aggregated or any individual pathology, causing the AUROC to be undefined for most bins.

Alternative measures of performance

We found consistent results as the ones introduced in earlier sections using alternative measures of performance. Specifically, in addition to using (1) absolute error and signed error with continuous ground truth probabilities and (2) AUROC as the metric for radiologist or AI performance, we conducted the proposed analyses using absolute error and signed error with binary ground truth labels, which were computed by thresholding the continuous ground truth probabilities at 50. Results are shown in Supplementary Tables 14 – 26 .

Under binary ground truth labels, the relationships between experienced-based characteristics and treatment effect found earlier under continuous ground truth probabilities held for all pathologies aggregated and individual pathologies (Supplementary Tables 16 – 19 ). The relationships between unassisted error and treatment effect held except for lesion, where it was not statistically significant under continuous ground truth probabilities but was significant under binary ones (Supplementary Table 20 ). The relationships between unassisted error and treatment effect without split sampling held except for edema, where it was statistically significant at a significance level of 0.05 under continuous ground truth probabilities but was significant at 0.01 under binary ground truth probabilities (Supplementary Table 21 ). The relationships between unassisted error and assisted error held except for rib fracture, where it was statistically significant at a significance level of 0.05 under continuous ground truth probabilities but was significant at 0.01 under binary ground truth probabilities (Supplementary Table 22 ). The relationships between AI error and treatment effect held for all pathologies aggregated (Supplementary Tables 23 and 25 ). The relationships on individual pathologies also varied as they did with continuous ground truth probabilities and did not show a singular trend.

The overall consistencies of the results presented show the general applicability of the findings to different ways of measuring radiologist and AI performance.

In this study, we investigated the heterogeneous treatment effects of AI assistance on radiologists for chest X-ray diagnosis. Our findings, based on a large-scale sample of 140 radiologists, highlight the existence of radiologist heterogeneity in treatment effects, which has substantial implications for both absolute and relative performance. These results underscore the inadequacy of a one-size-fits-all approach to AI assistance and emphasize the importance of individualized strategies to maximize benefits and minimize potential harms. Understanding who benefits from AI assistance and who is negatively impacted is crucial for effectively targeting AI assistance.

We found that experience-based radiologist characteristics, including years of experience, subspecialty in thoracic radiology and experience with AI tools, did not serve as reliable predictors of treatment effect, in terms of both calibration performance and discrimination performance. These findings challenge the associations between experience-based radiologist characteristics and the treatment effect of AI assistance reported in previous research 24 , 25 , 26 , 27 , 28 . The observed variability could be attributed to our larger and more diverse sample size, encompassing 140 radiologists with varying skill levels, experiences and preferences. Additionally, our study’s inclusion of a wide range of diagnostic tasks enables a robust examination of the complex factors influencing the treatment effect. Furthermore, the performance characteristics and quality of the specific AI system may play an important role, highlighting the need for developers to consider these factors when deploying AI assistance. To optimize the implementation of AI assistance, a comprehensive assessment of multiple factors, including the clinical task, patient population and AI system, is essential.

Similarly, direct measures of diagnostic skill, such as unassisted error, showed limited predictive power for treatment effects. This finding again holds for both calibration performance and discrimination performance. Surprisingly, radiologists who initially performed poorly without AI assistance did not necessarily benefit more or experience more harm from AI assistance compared to higher-performing counterparts. We demonstrate that proper use of statistical methods, such as split sampling, is crucial to avoid spurious associations between unassisted error and treatment effect and ensures reliable conclusions about the predictive power of unassisted error. Future research should consider cognitive abilities, adaptability to new technologies and decision-making processes as potential predictors. Developing accurate predictive models to identify radiologists who are more likely to benefit from AI assistance holds promise for future investigations. Without reliable predictors, it is necessary to measure radiologists’ response to AI assistance under realistic simulations of deployment settings before deciding whether to provide AI assistance to different radiologists. For example, it may be necessary to directly measure a radiologist’s treatment effect from the assistive AI system on an experimental dataset that is representative of the target patient population.

In addition to investigating the radiologist characteristics that can impact AI’s treatment effect, we showed that higher-quality AI assistance leads to better treatment effects in terms of calibration performance measured by absolute error, whereas the trend was unclear in terms of discrimination performance measured by AUROC. Our results indicate that AI predictions with smaller errors lead to better treatment effects on all pathologies aggregated, highlighting the importance of developing more accurate AI models for assistance. Conversely, AI predictions with large errors tend to lead to negative treatment effects, suggesting that radiologists struggle to consistently distinguish between accurate and inaccurate AI predictions and can be misled by inaccurate AI predictions. Moreover, we observed that, given the same absolute error, AI predictions that underestimate the ground truth probabilities can lead to better treatment effects than predictions that overestimate them on all pathologies aggregated. Apart from improving AI accuracy, it is valuable to help radiologists better identify inaccurate AI predictions. For example, assistive AI systems that provide explanations for their predictions 32 or generate nuanced radiology reports 33 , 34 , 35 , 36 , 37 , rather than probabilities alone, may allow radiologists to potentially extract value from inaccurate AI predictions. In addition, we emphasize that these findings between AI accuracy and treatment effect are the result of many factors simultaneously at play, including the ground truth probability, the radiologist’s predicted probability and how radiologists interpret and use AI assistance, which can all be correlated with AI’s predicted probability. Therefore, these findings should not be extrapolated for defining the cognitive mechanism in which AI assistance helps or hurts radiologists. Further research with explicit control of the potential factors is necessary to understand that underlying mechanism 29 .

Our study has several limitations that should be acknowledged. First, the randomization of treatment conditions in the experiment, although necessary to eliminate confounding factors, prevented the analysis of temporal trends in radiologists’ response to AI assistance. We were unable to assess whether radiologists improved in incorporating AI predictions over time as they encountered more patient cases. Future research should aim to investigate these evolving dynamics between radiologists and AI. Second, the AI assistance available to radiologists contained only predicted probabilities and did not include additional explanations, such as localization of pathologies, which could help radiologists more accurately interpret and, therefore, make better use of the available AI predictions. Designers of AI systems should investigate the optimal types of explanations to present and the mode of presentation while staying cautious of the increased cognitive burden that this additional information can bring. Another limitation is the lack of exploration into the impact of task granularity. The AI model generated predictions for 15 individual pathologies, some of which were interconnected and represented different levels of detail. For instance, airspace opacity encompasses pathologies such as atelectasis, edema and consolidation. Understanding the relationships between higher-level and lower-level pathologies would be valuable in future studies. Furthermore, due to the simultaneous presentation of all 15 AI predictions, it was challenging to isolate the effect of AI assistance on individual pathologies. The influence of AI predictions on one pathology could potentially affect the radiologists’ response to AI predictions on other pathologies, especially when they are interrelated. Additionally, because we provided actual AI predictions on patient cases to radiologists, it was also difficult to eliminate the confounding factor of the patient case when studying the relationship between the accuracy of AI predictions and the radiologist’s treatment effect. Future work may control for the influence of the patient case by providing artificially set predictions to radiologists.

In conclusion, our study underscores the need for individualized approaches that are aware of clinician heterogeneity, high-quality AI models and comprehensive assessments of multiple factors to optimize the implementation of AI assistance in clinical medicine. Collaboration between clinicians and AI developers, focusing on personalized strategies and continuous improvement of AI models, will be essential for achieving the full potential of clinician–AI collaboration in healthcare.

This research complied with all relevant ethical regulations. The study that produced the AI assistance dataset 29 used in this study was determined by the Massachusetts Institute of Technology (MIT) Committee on the Use of Humans as Experimental Subjects to be exempt through exempt determination E-2953.

Dataset specification

This study used 324 retrospective patient cases from Stanford University’s healthcare system containing chest X-rays and clinical histories, which include patients’ indication, vitals and labs. In this study, we analyzed data collected from a total of 140 radiologists participating in two experiment designs. The non-repeated-measure design included 107 radiologists in a non-repeated-measure setup (Supplementary Fig. 1 ). Each radiologist read 60 patient cases across four subsequences that each contained 15 cases. Each subsequence corresponded to one of four treatment conditions: with AI assistance and clinical histories, with AI assistance and without clinical history, without AI assistance and with clinical histories and without AI assistance and clinical histories. The four subsequences and associated treatment conditions were organized in a random order. The 60 patient cases were randomly selected and randomly assigned to one of the treatment conditions. This design included across-subject and within-subject variations in the treatment conditions; it did not allow within-case-subject comparisons because a case was encountered only once for a radiologist 38 . Order effects were mitigated by the randomization of treatment conditions. The repeated-measure design included 33 radiologists in a repeated-measure setup (Supplementary Fig. 2 ). Each radiologist read a total of 60 patient cases, each under each of the four treatment conditions and producing a total of 240 diagnoses. The radiologist completed the experiment in four sessions, and the radiologist read the same 60 randomly selected patient cases in each session under each of the various treatment arms. In each session, 15 cases were read in each treatment arm in batches of five cases. Treatments were randomly ordered. This resulted in the radiologist reading each patient case under a different treatment condition over the four sessions. There was a 2-week washout period 15 , 39 , 40 between every session to minimize order effects of radiologists reading the same case multiple times. This design included across-subject and within-subject variations as well as across-case-radiologist and within-case-radiologist variations in treatment conditions. Order effects were mitigated by the randomization of treatment conditions. No enrichment was applied to the data collection process. We combined data from both experiment designs from the clinical history conditions. Further details about the data collection process are available in a separate study 29 , which focuses on establishing a Bayesian framework for defining optimal human–AI collaboration and characterizing actual radiologist behavior in incorporating AI assistance. The study was determined exempt by the MIT Committee on the Use of Humans as Experimental Subjects through exempt determination E-2953.

There are 15 pathologies with corresponding AI predictions: abnormal, airspace opacity, atelectasis, bacterial/lobar pneumonia, cardiomediastinal abnormality, cardiomegaly, consolidation, edema, lesion, pleural effusion, pleural other, pneumothorax, rib fracture, shoulder fracture and support device hardware. These pathologies, the interrelations among these pathologies and additional pathologies without AI predictions can be visualized in a hierarchical structure in Supplementary Fig. B.1 . Radiologists were asked to familiarize themselves with the hierarchy before starting, had access to the figure throughout the experiment and had to provide predictions for pathologies following this hierarchy. This aimed to maximize clarity on the specific pathologies referenced in the experiment. When radiologists received AI assistance, they were simultaneously presented with the AI predictions for these 15 pathologies along with the patient’s chest X-ray and, if applicable, their clinical history. The AI predictions were presented in the form of prediction probabilities on a 0–100 scale. The AI predictions were generated by the CheXpert model 8 , which is a DenseNet121 (ref. 41 )-based model for chest X-rays that has been shown to perform similarly to board-certified radiologists. The model generated a single prediction for fracture that was used as the AI prediction for both rib fracture and shoulder fracture. Authors of the CheXpert model 8 decided on the 14 pathologies (with a single prediction for fracture) based on the prevalence of observations in radiology reports in the CheXpert dataset and clinical relevance, conforming to the Fleischner Society’s recommended glossary 42 whenever applicable. Among the pathologies, they included ‘Pneumonia’ (corresponding to ‘bacterial/lobar pneumonia’) to indicate the diagnosis of primary infection and ‘No Finding’ (corresponding to ‘abnormal’) to indicate the absence of all pathologies. These pathologies were set in the creation of the CheXpert labeler 8 , which has been applied to generate labels for reports in the CheXpert dataset and MIMIC-CXR 43 , which are among the largest chest X-ray datasets publicly available.

The ground truth probabilities for a patient case were determined by averaging the continuous predicted probabilities of five board-certified radiologists from Mount Sinai Hospital with at least 10 years of experience and chest radiology as a subspecialty on a 0–100 scale. For instance, if the predicted probabilities of the five board-certified radiologists are 91, 92, 92, 100 and 100, respectively, the ground truth probability is 95. The prevalence of the pathologies based on a ground truth probability threshold of 50 of a pathology being present is shown in Supplementary Table 1 .

The participating radiologists represent a diverse set of institutions recruited through two means. Their primary affiliations include large, medium and small clinical settings and non-clinical settings. Additionally, some radiologists are affiliated with an academic hospital, whereas others are not. Radiologists in the non-repeated-measure design were recruited from teleradiology companies. Radiologists in the repeated-measure design were recruited from the Vinmec health system in Vietnam. Details about the participating radiologists and recruitment process can be found in Supplementary Note | Participant recruitment and affiliation.

The experiment interface and instructions presented to participating radiologists can be found in Supplementary Note | Experiment interface and instructions. Before entering the experiment, radiologists were instructed to walk through the experiment instructions, the hierarchy of pathological findings, basic information and performance of the AI model, video demonstration of the experiment interface and examples, consent clauses, comprehension check questions, information on bonus payment that incentivizes effort and practice patient cases covering four treatment conditions and showing example AI predictions from the AI model used in the experiment.

Sex and gender statistics of the participating radiologists and patient cases are available in Supplementary Tables 39 and 40 , respectively. Sex and gender were not considered in the original data collection procedures. Disaggregated information about sex and gender at the individual level was collected in the separate study and will be made available 29 .

Empirical Bayes for individual heterogeneity

We used the empirical Bayes method 30 to shrink the raw mean heterogeneous treatment effects and performance metrics of individual radiologists measured on the dataset toward the grand mean to ameliorate overestimating heterogeneity due to sampling error. The values include AI’s treatment effects on error, sensitivity and specificity and performance metrics on unassisted error, sensitivity and specificity.

Assume that \({t}_{r}\) is radiologist r ’s true mean treatment effect from AI assistance or any metric of interest. We observe

which differs from \({t}_{r}\) by \({{{\eta }}}_{r}\) . We use a normal distribution as the prior distribution over the metric of interest. The mean of the prior distribution can be computed as

the mean of the observed mean metric of interest of radiologists. The variance of the prior distribution can be computed as

the variance of the observed mean metric of interest of radiologists minus the estimated \(E\left[{{{\eta }}}_{r}^{2}\right]\) . We can estimate \(E\left[{{{\eta }}}_{r}^{2}\right]\) with

Denote the estimated mean and variance of the prior distribution as \({{\rm{\mu }}}_{0}\) and \({{\rm{\sigma }}}_{0}^{2}\) . We can compute the mean of the posterior distribution for radiologist \(r\) as

where \({{\rm{\mu }}}_{r}=\widetilde{{t}}_{t}\) and \({{\rm{\sigma }}}_{r}=s.e.\left(\widetilde{{t}}_{r}\right)\) ; we can compute the variance of the posterior as

where \({{\rm{\sigma }}}_{r}=s.e.\left(\widetilde{{t}}_{r}\right)\) . The updated mean of the posterior distribution is the radiologist’s metric of interest after shrinkage.

For the analysis on treatment effects on absolute error, we focus on high-prevalence pathologies with prevalence greater than 10%, because radiologists’ baseline performance without AI assistance is generally highly accurate on low-prevalence pathologies, where they correctly predict that a pathology is not present, and, as a result, there is little variation in radiologists’ errors. This is especially true when computing each individual radiologist’s treatment effect. When there is zero variance in the performance of a radiologist under a treatment condition, the associated standard error estimate is zero, making it impossible to perform inference on this radiologist’s treatment effect.

Combined characteristics model for splitting radiologists into subgroups

The combined characteristics model was fitted on a training set of half of the radiologists ( n  = 68) to predict treatment effects of the test set of the remaining half ( n  = 68). The treatment effect predictions on the test set were used as the combined characteristics score for splitting the test set radiologists into binary subgroups (based on whether a particular radiologist’s combined characteristics score was smaller than or equal to the median treatment effect of radiologists computed from all available reads). Then, the same procedure was repeated after flipping the training set and test set radiologists to split the other set of radiologists into binary subgroups. The experience-based characteristics of radiologists in the randomly split training set and test set were balanced: one set contained 27 radiologists with less than or equal to 6 years of experience and 41 radiologists with more than 6 years of experience, and the other set contained 41 and 27, respectively. One set contained 47 radiologists who did not specialize in thoracic radiology and 21 radiologists who did, and the other set contained 54 and 14 radiologists, respectively. One set contained 32 radiologists without experience with AI tools and 36 radiologists with experience, and the other set contained 31 and 37, respectively.

Treatment effect models

To compute a radiologist’s observed mean treatment effect and the corresponding standard errors and the overall treatment effect of AI assistance across subgroups, we built a linear regression model with the following formulation using the statsmodels library: error   ∼   1  +  C(treatment) . Here, error refers to the absolute error of a radiologist prediction; 1 refers to an intercept term; and treatment refers to a binary indicator of whether the prediction is made with or without AI assistance. This formulation allows us to compute the treatment effect of AI assistance for both non-repeated-measure and repeated-measure data.

Subgroup-specific treatment effect models

For the analyses on experience-based radiologist characteristics and AI error, we computed the treatment effects of subgroups split based on the predictor of interest by building a linear regression model with the following formulation using the statsmodels library: error   ∼   1  +  C(subgroup)  +  C(treatment):C(subgroup) . Here, error refers to the absolute error of a radiologist prediction; 1 refers to an intercept term; subgroup refers to an indicator of the subgroup that the radiologist is split into; and treatment refers to a binary indicator of whether the prediction is made with or without AI assistance. This formulation allows us to compute the subgroup-specific treatment effect of AI assistance for both non-repeated-measure data and repeated-measure data.

Cluster-robust standard errors

To account for correlations of observations within patient cases and radiologists, we computed cluster-robust standard errors that are two-way clustered at the patient case and radiologist level for all inferences unless otherwise specified 44 , 45 . With the statsmodels library’s ordinary least squares (OLS) class, we used a clustered covariance estimator as the type of robust sandwich estimator and defined two-way groups based on identifiers of the patient cases and radiologists. The approach assumes that regression model errors are independent across clusters defined by the patient cases and radiologists and adjusts for correlations within clusters.

Reversion to the mean

The reversion to the mean effect and the mechanism of split sampling in avoiding reversion to the mean are explained in the following derivation:

Suppose that \({u}_{i,r}^{* }\) and \({a}_{i,r}^{* }\) are the true unassisted and assisted diagnostic error of radiologist \(r\) on patient case i . Suppose that we measure \({u}_{i,r}={u}_{i,r}^{* }+{e}_{i,r}^{u}\) and \({a}_{i,r}={a}_{i,r}^{* }+{e}_{i,r}^{a}\) where \({e}_{i,r}^{u}\) and \({e}_{i,r}^{a}\) are measurement errors. Assume that the measurement errors are independent of \({u}_{i,r}^{* }\) and \({a}_{i,r}^{* }\) .

To study the relationship between unassisted error and treatment effect, we intend to build the following linear regression model:

where the error is independent of the independent variable, and \({u}_{r}^{* }\) and \({a}_{r}^{* }\) are the mean unassisted and assisted performance of radiologist \(r\) . Here, the moment condition

is as desired. This univariate regression estimates the true value of \({{\beta }}\) , which is defined as

However, because we have access only to noisy measurements \({u}_{r}\) and \({a}_{r}\) , consider instead an approach that builds the model

and assumes the moment condition

This linear regression model using noisy measurements instead generates the following estimate of \({{\beta }}\) :

which is incorrect because of the additional \({{V}}\,{{ar}}\left({{{e}}}_{{{r}}}^{{{u}}}\right)\) terms in the numerator and the denominator. The additional term in the denominator represents attenuation bias, which we address in detail in a later subsection. The term in the numerator represents the reversion to the mean issue, which we now discuss in further detail.

As the equation shows, the bias caused by reversion to the mean is positive. This term exists because the moment condition \(E\left[{e}_{r}\times {u}_{r}\right]=0\) , equation ( 11 ), is not valid at the true value of \({{\beta }}\) as shown in the following derivation:

Split sampling solves this bias by using separate patient cases for computing unassisted error and treatment effect. A simple construction of split sampling is to use a separate case i for computing the treatment effect and using the remaining cases to compute unassisted error. With this construction, we obtain the following estimate of \({{\beta }}\) :

where \({u}_{i,r}\) is the unassisted performance on case i for radiologist \(r\) , and \({u}_{\ne i,r}\) is the mean unassisted performance computed on all unassisted cases other than i . If the errors on each case used to compute \({u}_{r}^{* }\) and \({a}_{r}^{* }\) are independent, the estimate of \({{\beta }}\) is equal to

The remaining discrepancy in the denominator again represents attenuation bias and is addressed in a later subsection.

Data efficient split sampling construction

To study unassisted error as a predictor of treatment effect, we built a linear regression model with the following formulation using the statsmodels library: treatment effect   ∼   1   +   unassisted error . We designed the following split sampling construction to maximize data efficiency when computing the independent and dependent variables in the linear regression.

Let i index a patient case and \(r\) index a radiologist. Assume that a radiologist reads \({N}_{u}\) cases unassisted and \({N}_{a}\) cases assisted. Recall that the unassisted and assisted cases are disjoint for the non-repeated-measure data; they overlap exactly for the repeated-measure data.

For the non-repeated-measure design, we adopt the following construction:

where \({x}_{\ne i,r}=\frac{1}{{N}_{u}-1}{\sum }_{k\ne i}{u}_{k,r}\) and \({a}_{r}=\frac{1}{{N}_{a}}{\sum }_{k}{a}_{k,r}\) . Here, \({x}_{\ne i,r}\) is the mean unassisted performance computed on all unassisted cases other than i ; \({u}_{{i},{r}}\) is the unassisted performance on case i for radiologist \(r\) ; and \({a}_{r}\) is the mean assisted performance on all assisted cases for radiologist \(r\) .

For the repeated-measure design, we adopt the following construction:

where \({x}_{\ne i,r}=\frac{1}{{N}_{u}-1}{\sum }_{k\ne i}{u}_{k,r}\) . Here, \({x}_{\ne i,r}\) is the mean unassisted performance computed on all cases other than i ; \({u}_{i,r}\) is the unassisted performance on case i for radiologist \(r\) ; and \({a}_{i,r}\) is the assisted performance on case i for radiologist \(r\) .

To study unassisted error as a predictor of assisted error, we built a linear regression model with the following formulation using the statsmodels library: assisted error   ∼   1   +   unassisted error . We designed the following split sampling construction that maximizes data efficiency when computing the independent and dependent variables in the linear regression.

where \({x}_{r}=\frac{1}{{N}_{u}}{\sum }_{k}\,{x}_{k,r}\) . Here, \({x}_{r}\) is the mean unassisted performance computed on all unassisted cases, and \({a}_{i,r}\) is the assisted performance on case i for radiologist \(r\) .

where \({x}_{\ne i,r}=\frac{1}{{N}_{u}-1}{\sum }_{k\ne i}{u}_{k,r}\) . Here, \({x}_{\ne i,r}\) is the mean unassisted performance computed on all unassisted cases other than i and \({a}_{i,r}\) is the assisted performance on case i for radiologist \(r\) .

The constructions above again emphasize the necessity for split sampling. Without split sampling, the mean unassisted performance, which is the independent variable of the linear regression, will be correlated with the error terms due to overlapping patient cases, leading to a bias in the regression.

Adjustment for attenuation bias

We adjusted for attenuation bias for the split sampling linear regression formulations.

We want to estimate regressions of the form

where \({Y}_{r}\) is an outcome for radiologist \(r\) and \(E\left[{x}_{r}\right]\) is radiologist \(r\) ʼs average unassisted performance. We observe

where \({{{\eta }}}_{r}=\frac{1}{{N}_{r}}\mathop{\sum }\limits_{i}{x}_{{ir}}-E\left[{x}_{r}\right]\) and \(E\left[{{{\eta }}}_{r}{x}_{r}\right]=0\) and \(E\left[{{{\eta }}}_{r}{{\rm{\varepsilon }}}_{r}\right]=0\) , which are justified by independent and identically distributed (i.i.d.) sampling of cases and split sampling, respectively.

Using observations from the experiment, we estimate the following regression:

Recall that

where \({\rm{\lambda }}=\frac{E\left[{\left({x}_{r}-E\left[{x}_{r}\right]\right)}^{2}\right]}{E\left[{\left({x}_{r}-E\left[{x}_{r}\right]\right)}^{2}\right]+E\left[{{{\eta }}}_{r}^{2}\right]}\) and \({{{\beta }}}_{1}=\frac{E\left[\left({x}_{r}-E\left[{x}_{r}\right]\right)\left({Y}_{r}-E\left[{Y}_{r}\right]\right)\right]}{E\left[{\left({x}_{r}-E\left[{x}_{r}\right]\right)}^{2}\right]}\) . We can estimate \({\rm{\lambda }}\) using a plug-in estimator for each term in the data: (1)

This is the standard error of the mean estimator. (2)

which can be estimated by taking the difference between the variance of the observed \(\widetilde{{x}}_{r}\) ’s and the estimated \(E\left[{{{\eta }}}_{r}^{2}\right]\) . The denominator of \({\rm{\lambda }}\) is effectively \(E\left[{\left(\tilde{x}_{r}-E\left[\tilde{x}_{r}\right]\right)}^{2}\right]\) .

Finally, with \(\hat{{\rm{\lambda }}}\) , we can estimate \({{{\beta }}}_{1}\) using the estimator

For inference, notice that \(\sqrt{n}\left({{\hat{\rm{\gamma }}}_{1}}-{{\rm{\gamma }}}_{1}\right){\to }^{d}N\left(0,{{\rm{\sigma }}}_{{\rm{\gamma }}}^{2}\right)\) and \(\hat{{\rm{\lambda }}}{\to }^{p}\,{\rm{\lambda }}\) . By Slutsky’s theorem, we know that

Therefore, we divide the standard errors of \({{\hat{\rm{\gamma }}}_{1}}\) by \(\hat{{\rm{\lambda }}}\) to obtain the standard errors of \({{{\hat{\beta }}}_{1}}\) .

This concludes the adjustment for attenuation bias for the slope term.

Statistical testing

To determine the amount of heterogeneity between subgroups of radiologists receiving lower versus higher treatment effects, we ran an unpaired t -test between the two subgroups of treatment effects computed using the empirical Bayes method. We used the Wald test to test regression coefficients against the null hypothesis of joint equality among treatment effects of different subgroups to determine if there is a statistically significant difference among subgroups split based on the predictor of interest. We also used the Wald test to test regression coefficients against the null hypothesis of zero to determine in a continuous analysis if the independent variable, namely unassisted error, is a predictor of the dependent variable, namely treatment effect or assisted error. We used the Benjamini–Hochberg procedure to correct for multiple hypothesis testing over 15 individual pathologies. For the analysis on treatment effect on AUROC between subgroups determined by AI error (Supplementary Table 34 ), we conducted an F -test to determine whether there is a statistically significant difference between treatment effects on AUROC in different bins. Specifically, we used the number of reads that fall into each bin as the group size. We used the grand mean AUROC and group AUROCs along with group sizes to compute the sum of squares between; we used the estimated standard error of each group AUROC along with the group size to compute the sum of squares within (error).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The 324 patient cases from Stanford University’s healthcare system were used under licensing. They are available at https://stanfordaimi.azurewebsites.net/datasets/5194008e-61cf-4083-9896-3d4bd8bf8b0b , conditioned on a Stanford University data research use agreement. The AI predictions used in the experiment were generated by the CheXpert model trained on the CheXpert dataset 8 , which is publicly available. The clinician–AI collaboration dataset is available at https://osf.io/z7apq/ upon request for access at the Open Science Framework dataset page.

Code availability

Code for the analysis is available at https://doi.org/10.5281/zenodo.10467492 (ref. 46 ). Data analysis was conducted using Python 3.9.7, libraries statsmodels 0.13.5 and scipy 1.10.1; and R 4.1.3 and libraries MRMCaov 0.3.0 and auctestr 1.0.0.

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Acknowledgements

The authors acknowledge support from the Alfred P. Sloan Foundation (2022-17182, N.A.), the J-PAL Healthcare Delivery Initiative and the MIT School of Humanities, Arts, and Social Sciences (SHASS).

Author information

These authors contributed equally: Feiyang Yu, Alex Moehring.

These authors jointly supervised this work: Tobias Salz, Nikhil Agarwal, Pranav Rajpurkar.

Authors and Affiliations

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Feiyang Yu, Oishi Banerjee & Pranav Rajpurkar

Department of Computer Science, Stanford University, Stanford, CA, USA

Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA

Alex Moehring

Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA

Tobias Salz & Nikhil Agarwal

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Contributions

T.S., N.A. and P.R. conceived the study. F.Y. and A.M. planned and executed the data analysis. F.Y., A.M., O.B., T.S., N.A. and P.R. contributed to the interpretation of findings. F.Y. and O.B. drafted the manuscript. All authors provided critical feedback and substantially contributed to the revision of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Pranav Rajpurkar .

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The authors declare no competing interests.

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Nature Medicine thanks Jarrel Seah, Michael Sjoding and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lorenzo Righetto, in collaboration with the Nature Medicine team.

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Extended data

Extended data fig. 1 individual heterogeneity in treatment effects..

a , b , Individual heterogeneity in treatment effects of 140 radiologists as determined using the empirical Bayes method on ( a ) all pathologies aggregated and ( b ) high-prevalence pathology labels (pathology labels with greater than 10% prevalence). The curve is the kernel density estimate (KDE).

Extended Data Fig. 2 Individual heterogeneity in unassisted error.

a , b , Individual heterogeneity in unassisted error of 140 radiologists as determined using the empirical Bayes method on ( a ) all pathologies aggregated and ( b ) high-prevalence pathology labels (pathology labels with greater than 10% prevalence). The curve is the kernel density estimate (KDE).

Extended Data Fig. 3 Individual heterogeneity in treatment effects on sensitivity, sensitivities, treatment effects on specificity, and specificities.

a-b , Individual heterogeneity in ( a ) improvement in sensitivities, ( b ) sensitivity, ( c ) improvement in specificity, and ( d ) specificity of 140 radiologists as determined using the empirical Bayes method on all pathologies aggregated. The curve is the kernel density estimate (KDE).

Extended Data Fig. 4 Conventional radiologist characteristics as indicators for treatment effect on individual pathologies.

a , Difference in treatment effects of subgroups of radiologists on high-prevalence pathology labels (pathology labels with greater than 10% prevalence). The difference is computed between lower and higher improvement subgroups. The error bars show 95% confidence intervals. There are statistically significant differences between subgroups on high-prevalence pathology labels (abnormal B-H adjusted P = 1.66e-29, airspace opacity B-H adjusted P = 7.20e-29, atelectasis B-H adjusted P = 1.10e-30, cardiomediastinal abnormality B-H adjusted P = 4.85e-30, support device hardware B-H adjusted P = 1.57e-30; B-H adjusted P < 0.001). A two-sided, unpaired t-test between the two subgroups of treatment effects was conducted. The difference is -4.194 (95% CI: -4.753 to -3.636) for abnormal, -1.465 (95% CI: -1.664 to -1.266) for airspace opacity, -1.766 (95% CI: -1.991 to -1.541) for atelectasis, -1.571 (95% CI: -1.777 to -1.365) for cardiomediastinal abnormality, and -3.150 (95% CI: -3.552 to -2.748) for support device hardware. 136 radiologists with available survey data are used. b , Difference in treatment effects of subgroups of radiologists based on combined characteristics of years of experience, subspecialty in thoracic radiology and experience with AI tools on held-out test sets of radiologists. The difference is computed between lower and higher predicted improvement subgroups. The error bars show 95% confidence intervals. n.s. indicates no statistical significance (B-H adjusted P > 0.05). The Wald test was used to test regression coefficients that estimate treatment effects against the null hypothesis of joint equality among treatment effects of different subgroups. Details of the statistical models are available in Methods. There are 136 radiologists with available survey data on the three characteristics. c-e , Difference in treatment effects of subgroups of radiologists based on ( c ) years of experience, ( d ) subspecialty in thoracic radiology, and ( e ) experience with AI tools on 15 individual pathologies. The difference is computed between ( c ) subgroups of fewer versus more years of experience, ( d ) subgroups without versus with subspecialty in thoracic radiology, and ( e ) subgroups without versus with experience using AI tools. The error bars show 95% confidence intervals. n.s. indicates no statistical significance (B-H adjusted P > 0.05). The same statistical test as in b was used. There are 136 radiologists with available survey data.

Extended Data Fig. 5 Conventional radiologist characteristics as indicators for treatment effect on AUROC on individual pathologies.

a , Difference in treatment effects on AUROC of subgroups of radiologists based on combined characteristics of years of experience, subspecialty in thoracic radiology and experience with AI tools on held-out test sets of radiologists. The difference is computed between lower and higher predicted improvement subgroups. The error bars show 95% confidence intervals. n.s. indicates no statistical significance (B-H adjusted P > 0.05). The difference is 0.034 (95% CI: -0.017 to 0.842) for abnormal and -0.023 (95% CI: -0.082 to 0.035) for airspace opacity. The Wald test was used to test regression coefficients that estimate treatment effects against the null hypothesis of joint equality among treatment effects of different subgroups. Details of the statistical models are available in Methods. 136 radiologists with available survey data are used. b-d , Difference in treatment effects of subgroups of radiologists based on ( b ) years of experience, ( c ) subspecialty in thoracic radiology, and ( d ) experience with AI tools on 2 individual pathologies on which the AUROC analysis could be computed. The difference is computed between ( b ) subgroups of fewer versus more years of experience, ( c ) subgroups without versus with subspecialty in thoracic radiology, and ( d ) subgroups without versus with experience using AI tools. The error bars show 95% confidence intervals. n.s. indicates no statistical significance (B-H adjusted P > 0.05). The same statistical test as in a was used. There are 136 radiologists with available survey data.

Supplementary information

Supplementary information.

Supplementary Tables 1–40, Supplementary Figs. 1 and 2, Supplementary Notes ‘Statistical modeling for AUROC analysis’ | ‘Participant recruitment and affiliation’ (contains Supplementary Tables A.1–4) | ‘Experiment interface and instructions’ (contains Supplementary Figs. B.1–7) and Supplementary References

Reporting Summary

Supplementary video.

Experiment instructions video presented to participating radiologists

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Yu, F., Moehring, A., Banerjee, O. et al. Heterogeneity and predictors of the effects of AI assistance on radiologists. Nat Med 30 , 837–849 (2024). https://doi.org/10.1038/s41591-024-02850-w

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Received : 22 June 2023

Accepted : 01 February 2024

Published : 19 March 2024

Issue Date : March 2024

DOI : https://doi.org/10.1038/s41591-024-02850-w

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