What Should the AI Era Doctor Know? A Scoping Review of Proposed Artificial Intelligence Competencies for Medical Education

What Should the AI Era Doctor Know? A Scoping Review of Proposed Artificial Intelligence Competencies for Medical Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review What Should the AI Era Doctor Know? A Scoping Review of Proposed Artificial Intelligence Competencies for Medical Education Victor M. Hunt, Laurine K. Sprehe, Weston C. de Lomba, Rodrigo R. Gameiro, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9045167/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background : Artificial intelligence (AI) is rapidly reshaping healthcare and the competencies expected of graduating medical students. Existing AI curricula and competency recommendations for undergraduate medical education (UME) are fragmented, and prior reviews have largely described broad themes or educational programs rather than specifying competency-level outcomes. Objectives : To systematically map and synthesize proposed AI competencies for UME. Eligibility criteria: Peer-reviewed sources proposing original AI-related competencies or learning objectives explicitly intended for undergraduate medical students. Sources of evidence : PubMed, Embase, Web of Science, and ERIC from inception to July 28, 2025, without language limits, supplemented by reference screening. Charting methods : Multiple reviewers independently screened sources and extracted verbatim competency-relevant text. Text was decomposed into discrete statements describing single AI-related skills or knowledge areas, then labelled using an agreed-upon rubric as domains, competencies, or learning objectives; statement frequencies were summarized to identify convergent areas, evidence gaps, and cross-competency relationships. Results : Of 4,071 records identified and duplicates removed, 2,877 titles/abstracts were screened and 367 full texts were assessed for eligibility. Fifty-four studies from 22 countries met inclusion criteria. From these, 564 competency-relevant statements were synthesized into a taxonomy comprising 7 domains—AI Ethics, AI Law and Regulation, AI Professionalism in Healthcare, Clinical Applications of AI, Critical Appraisal of AI Output, Research and Innovation in AI, and Theory and Foundations of AI—spanning 37 competencies and 170 learning objectives. Most sources were recent, editorial or opinion-based, and described theoretical rather than fully implemented or evaluated curricula, but showed substantial convergence on ethical/legal oversight, critical appraisal of AI outputs, and foundational understanding of AI methods and data. Conclusions : This scoping review provides a hierarchical synthesis of AI-related competencies for UME, offering a structured foundation for curriculum design and evaluation and underscoring the need for stakeholder collaboration to refine and implement a standardized and internationally actionable curriculum. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Scientific community and society/Scientific community Scientific community and society/Social sciences Full Text Additional Declarations Competing interest reported. J.H.C. reports research funding support in part from the NIH/National Institute of Allergy and Infectious Diseases (1R01AI17812101), NIH-NCATS Clinical and Translational Science Award (UM1TR004921), Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program (IIP) (R12), NIH/Center for Undiagnosed Diseases at Stanford (U01 NS134358), Stanford RAISE Health Seed Grant (2024), the Josiah Macy Jr. Foundation (AI in Medical Education), and the Stanford CARE AI Scholar Fellowship. J.H.C. is a co-founder of Reaction Explorer LLC, which develops and licenses organic chemistry education software; has received paid medical expert witness fees from Elite Experts; and has received one-time honoraria or travel expenses for invited presentations from insitro, General Reinsurance Corporation, AASCIF, and other industry conferences, academic institutions, and health systems. All other authors declare no financial or non-financial competing interests. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 12 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 05 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9045167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":604718478,"identity":"c787edb4-df0b-4b96-b54f-53f492b4b34a","order_by":0,"name":"Victor M. 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J.H.C. reports research funding support in part from the NIH/National Institute of Allergy and Infectious Diseases (1R01AI17812101), NIH-NCATS Clinical and Translational Science Award (UM1TR004921), Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program (IIP) (R12), NIH/Center for Undiagnosed Diseases at Stanford (U01 NS134358), Stanford RAISE Health Seed Grant (2024), the Josiah Macy Jr. Foundation (AI in Medical Education), and the Stanford CARE AI Scholar Fellowship. J.H.C. is a co-founder of Reaction Explorer LLC, which develops and licenses organic chemistry education software; has received paid medical expert witness fees from Elite Experts; and has received one-time honoraria or travel expenses for invited presentations from insitro, General Reinsurance Corporation, AASCIF, and other industry conferences, academic institutions, and health systems. All other authors declare no financial or non-financial competing interests.","formattedTitle":"What Should the AI Era Doctor Know? A Scoping Review of Proposed Artificial Intelligence Competencies for Medical Education","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9045167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9045167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eBackground\u003c/u\u003e: Artificial intelligence (AI) is rapidly reshaping healthcare and the competencies expected of graduating medical students. Existing AI curricula and competency recommendations for undergraduate medical education (UME) are fragmented, and prior reviews have largely described broad themes or educational programs rather than specifying competency-level outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eObjectives\u003c/u\u003e: To systematically map and synthesize proposed AI competencies for UME.\u003c/p\u003e\n\u003cp\u003eEligibility criteria: Peer-reviewed sources proposing original AI-related competencies or learning objectives explicitly intended for undergraduate medical students.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSources of evidence\u003c/u\u003e: PubMed, Embase, Web of Science, and ERIC from inception to July 28, 2025, without language limits, supplemented by reference screening.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCharting methods\u003c/u\u003e: Multiple reviewers independently screened sources and extracted verbatim competency-relevant text. Text was decomposed into discrete statements describing single AI-related skills or knowledge areas, then labelled using an agreed-upon rubric as domains, competencies, or learning objectives; statement frequencies were summarized to identify convergent areas, evidence gaps, and cross-competency relationships.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eResults\u003c/u\u003e: Of 4,071 records identified and duplicates removed, 2,877 titles/abstracts were screened and 367 full texts were assessed for eligibility. Fifty-four studies from 22 countries met inclusion criteria. From these, 564 competency-relevant statements were synthesized into a taxonomy comprising 7 domains—AI Ethics, AI Law and Regulation, AI Professionalism in Healthcare, Clinical Applications of AI, Critical Appraisal of AI Output, Research and Innovation in AI, and Theory and Foundations of AI—spanning 37 competencies and 170 learning objectives. 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