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Cited by 33Innovations in Medical EducationArtificial Intelligence in Healthcare and EducationHealth and Medical Education

Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence

Open access
Jacobs, Sarah Marie, Lundy, Neva Nicole, Issenberg, Saul Barry, Chandran, Latha·2023·JMIR Medical Education
DOI 10.2196/50903PMC PMC10762622

Published version of a preprint

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Abstract

The proliferation of generative artificial intelligence (AI) and its extensive potential for integration into many aspects of health care signal a transformational shift within the health care environment. In this context, medical education must evolve to ensure that medical trainees are adequately prepared to navigate the rapidly changing health care landscape. Medical education has moved toward a competency-based education paradigm, leading the Association of American Medical Colleges (AAMC) to define a set of Entrustable Professional Activities (EPAs) as its practical operational framework in undergraduate medical education. The AAMC’s 13 core EPAs for entering residencies have been implemented with varying levels of success across medical schools. In this paper, we critically assess the existing core EPAs in the context of rapid AI integration in medicine. We identify EPAs that require refinement, redefinition, or comprehensive change to align with the emerging trends in health care. Moreover, this perspective proposes a set of “emerging” EPAs, informed by the changing landscape and capabilities presented by generative AI technologies. We provide a practical evaluation of the EPAs, alongside actionable recommendations on how medical education, viewed through the lens of the AAMC EPAs, can adapt and remain relevant amid rapid technological advancements. By leveraging the transformative potential of AI, we can reshape medical education to align with an AI-integrated future of medicine. This approach will help equip future health care professionals with technological competence and adaptive skills to meet the dynamic and evolving demands in health care.

AI snapshot

Written from the paper’s full text.

TL;DR

This perspective paper critically examines the AAMC's 13 core Entrustable Professional Activities (EPAs) for undergraduate medical education through the lens of generative AI integration, arguing that some EPAs need refinement, redefinition, or replacement. The authors propose three new "emerging" EPAs—proficient use of health care technologies, understanding and applying health informatics, and transdisciplinary team skills—and predict differential AI impact across existing EPA clusters.

Key findings

  • The authors map AI's likely influence onto the AAMC's four EPA clusters (Core of the Core, Advanced, Aspirational, Emerging), arguing AI will have the most significant impact on the "Aspirational" cluster (EPAs 10-13) because generative AI can rapidly classify and transform large data sets, while the "Advanced" cluster (EPAs 3, 4, 8) may be less impacted as it focuses on practical clinical tasks.
  • Three new emerging EPAs are proposed: EPA 14 (proficient use of health care technologies), EPA 15 (understanding and applying health informatics), and EPA 16 (demonstrating team skills for transdisciplinary interactions in health care settings), plus an undefined future category (eg, ethically managing autonomous AI systems, environmental data-driven health care).
  • The authors predict EPA 14 and EPA 15 will eventually move into the "Core of the Core" cluster and EPA 16 into the "Aspirational" cluster, though they note these are conceptual suggestions only.
  • EPA 11 (obtaining informed consent) is singled out as needing redefinition, since learners will need to explain opaque AI-derived tools and prognoses to patients—an added communication burden on top of existing challenges.

Methods

  • Design: a conceptual/perspective analysis, not an empirical study; the authors state "there are no papers, to our knowledge, that directly apply these positions to the current UME frameworks of instruction."
  • Data/sample: the AAMC's 13 core EPAs and the AAMC Core EPAs Pilot Implementation Program's four-cluster categorization (Core of the Core, Advanced, Aspirational, Emerging), as reported by Amiel et al.
  • Analysis: categorical, cluster-by-cluster appraisal of how AI integration may change how each EPA is learned, taught, assessed, or redefined, with proposed new EPAs presented in Table 1.

Limitations

  • The paper states that the emerging EPA cluster "is yet to be fully delineated" and that it is "impossible to predict the entire list of such emerging EPAs"; the proposed cluster placements (eg, EPA 14/15 into Core of the Core) are explicitly labeled "only conceptual suggestions."
  • The paper notes implementation challenges including lack of standardization of AI definitions, lack of faculty expertise, absence of accreditation guidelines on AI in medical education, and limited data on AI assessment accuracy and consistency.
  • Evident from the text (not framed as a limitation by the authors): the paper presents no empirical data, no sample, and no formal validation of the proposed EPAs—it is an expert perspective with illustrative examples rather than tested outcomes.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.2196/50903

Cites

Statistics

Nothing to recompute.

Claims the paper makes

0 of 63 cited sources checked · 0 supported · 0 flagged

  • Interpretationcitation

    AI-based simulation technologies could provide students more opportunities to practice in a controlled environment.

    “For instance, AI-based simulation technologies could provide students more opportunities to practice in a controlled environment, and AI-assisted assessment tools could offer more objective and consistent feedback [19].”
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  • Interpretationcitation

    AI-assisted assessment tools could offer more objective and consistent feedback.

    “For instance, AI-based simulation technologies could provide students more opportunities to practice in a controlled environment, and AI-assisted assessment tools could offer more objective and consistent feedback [19].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
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  • Premisecitation

    The pilot group identified 6 EPAs (EPAs 1, 2, 5, 6, 7, and 9) as "Core of the Core" that are taught consistently and assessed efficiently within existing curricula of participating schools.

    “Among the 13 EPAs, the pilot group identified 6 EPAs (EPAs 1, 2, 5, 6, 7, and 9) that are taught consistently and assessed efficiently within the existing curricula of participating schools. They titled this group the “Core of the Core.””
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • Premisecitation

    The Aspirational cluster (EPAs 10-13) appears absent or underdeveloped in most participating schools' UME curricula.

    “The third cluster, identified as “Aspirational” (EPAs 10-13), appears to be absent or underdeveloped in most of the participating schools’ UME curricula [17].”
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • Interpretationreasoning

    AI will have the most significant impact on the "Aspirational" cluster of EPAs.

    “For example, we believe that given the ability of generative AI to rapidly classify and transform large sets of data, AI will have the most significant impact on the “Aspirational” cluster of EPAs.”
    Quote in the paperNot a substantial claim
  • May combine two claims. 15
  • Classified as probably not a substantial claim. 22

  • May combine two claims.

    Interpretationcitation

    AI could potentially expedite students' proficiency and entrustability achievement, enabling time-variable graduation into the health care workforce.

    “These skills are essential for medical trainees before residency and AI could potentially expedite students’ proficiency and entrustability achievement, enabling time-variable graduation into the health care workforce [20-23].”
    Quote in the paperNot a substantial claim0 of 4 sources checked
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    Interpretationcitation

    AI enables immediate, personalized feedback for learners, likely resulting in more efficient and responsive learning.

    “AI also enables immediate, personalized feedback for learners, likely resulting in more efficient and responsive learning [14,20].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
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  • Classified as probably not a substantial claim.

    Premisecitation

    The AAMC defined 13 core Entrustable Professional Activities (EPAs) for entering residency training.

    “In 2013, the Association of American Medical Colleges (AAMC) built upon the ACGME competency framework by defining 13 core Entrustable Professional Activities (EPAs) that outline the skills and competencies that every US medical school graduate should be entrusted with when entering residency training [5].”
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The pilot team suggested a fourth "Emerging" cluster of EPAs (such as telemedicine) not originally included in the AAMC's list but increasingly important to the medical student role.

    “The pilot team also suggested the existence of a fourth “Emerging” cluster of EPAs (such as telemedicine) that are not originally included in the AAMC’s list but nevertheless becoming increasingly more important to the medical student role.”
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    Premisecitation

    EPAs 3, 4, and 8 were titled "Advanced" and were felt to be most prominently represented in senior UME curricula.

    “The second grouping of EPAs 3, 4, and 8 was titled “Advanced,” and it was felt to be the most prominently represented in senior UME curricula.”
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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Over time, several new emerging EPAs will become part of the "Core of the Core" cluster.

    “Over time, we envision several of these new emerging EPAs becoming part of the “Core of the Core” cluster.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    EPAs in the "Advanced" cluster may be less impacted by AI implementation because they focus on more practical clinical tasks.

    “whereas EPAs in the “Advanced” cluster may be less impacted by AI implementation as they focus on more practical clinical tasks.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    A fourth cluster, "Emerging EPAs," will develop and continually evolve in response to new demands triggered by integration of technology and AI in health care.

    “In addition, we believe a fourth cluster, termed “Emerging EPAs,” and designated by a ring around the other clusters, will develop and continually evolve in response to the new demands triggered by the integration of technology and AI in health care.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    AI could be used to analyze system failures and pinpoint improvement opportunities, thus promoting a culture of safety and continuous learning.

    “Moreover, AI could be used to analyze system failures and pinpoint improvement opportunities, thus promoting a culture of safety and continuous learning [27].”
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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    If physicians and learners find explaining medical complexities challenging in the existing landscape, adding an additional technological layer could further complicate patient and physician communication.

    “If physicians and learners find explaining medical complexities challenging in the existing landscape, adding an additional technological layer could further complicate patient and physician communication.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    AI presents a dynamic landscape where clinicians need a fundamental understanding of these technologies and communication skills to relay information to patients.

    “As this paper underscores, AI presents a dynamic landscape where clinicians need a fundamental understanding of these technologies and communication skills to relay information to patients.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Other educators have proposed using AI to analyze and teach communication in health care.

    “while other educators have proposed using AI to analyze and teach communication in health care [42].”
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  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    The existing EPAs, while comprehensive, do not specifically address the use of health care technology.

    “The existing EPAs, while comprehensive, do not specifically address the use of health care technology.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Integration of AI allows practitioners to glean meaningful information and patterns rapidly, thus expediting efficiency and effectiveness of health care systems, which will be beneficial in learning and assuring safe and quality patient care.

    “The integration of AI allows practitioners to glean meaningful information and patterns rapidly thus expediting the efficiency and effectiveness of health care systems, which will be beneficial in the learning process and in assuring safe and quality patient care [30].”
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    Premisecitation

    In the United States, patient data are stored digitally in HIPAA-compliant electronic medical records.

    “In the United States, patient data are stored digitally in Health Insurance and Portability and Accountability Act (HIPAA)–compliant electronic medical records, and while medical records are capable of trending individual variables for laboratory tests and vital signs, effective systems to integrate multiple data points and key health trends over time do not currently exist [28].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
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  • Classified as probably not a substantial claim.

    Interpretationnone given

    The communication skills required to elicit information from a patient or communicate with other health care providers are different than the ability to explain complex medical concepts at a level patients and family can understand sufficiently to make life-changing medical decisions.

    “The communication skills required to elicit information from the patient (as done in EPA 1 when taking a history and physical) or communicate with other health care providers (EPAs 6 and 9) are different than the ability to explain complex medical concepts at a level that patients and family can understand sufficiently to make life-changing medical decisions.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    Integration of AI into the Aspirational cluster will move us closer toward realizing the goals of the Institute for Health Care Improvement's Triple Aim initiative.

    “We believe that the integration of AI into this cluster will move us closer toward realizing the goals of the Institute for Health Care Improvement’s Triple Aim initiative, which focuses on the experience of care, population health, and cost-effectiveness [34].”
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    Interpretationcitation

    Learners will need to understand how AI technologies function and their use in patient care.

    “Learners will need to understand how AI technologies function and their use in patient care [35,36].”
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    Premisecitation

    Explaining technology is not new; it is a challenge encountered whenever complex technology integrates into medical care.

    “Explaining technology is not new; it is a challenge encountered whenever complex technology integrates into medical care [44].”
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    Premisecitation

    Health care is increasingly data driven, with vast amounts of health data generated for various purposes, from clinical decision-making to quality improvement and research.

    “Health care is increasingly data driven, with vast amounts of health data generated for various purposes, from clinical decision-making to quality improvement and research [44].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [44] source

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    Premisecitation

    As early as 2018, the American Medical Association recommended incorporating AI training in medical education.

    “As early as 2018, the American Medical Association (AMA) recommended incorporating AI training in medical education [49].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [49] BerkowitzCReports of the council on medical education: augmented intelligence in medical education (resolution 317-A-18)CME Report 4-A-1920192023-12-12Chicago, IllAmerican Medical Associationhttps://www.ama-assn.org/system/files/cme-report-4-a19-annotated.pdf

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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The AAMC has demonstrated leadership in developing the core EPAs for CBME, as well as played a crucial role in developing diversity, equity, and inclusion competencies across the learning continuum.

    “The AAMC has demonstrated leadership in developing the core EPAs for CBME, as well as played a crucial role in developing diversity, equity, and inclusion competencies across the learning continuum [51].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [51] Diversity, equity, and inclusion competencies across the learning continuumAAMC20222023-12-12Washington, DCAAMChttps://www.aamc.org/data-reports/report/diversity-equity-and-inclusion-competencies-across-learning-continuum

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  • May combine two claims.

    Premisecitation

    As AI is brought further into educational tools, there is concern for the amount of data being collected about students and student data privacy, as the owners of the tools rather than the educational institutions become the owners of the data.

    “As AI is brought further into educational tools, there is concern for the amount of data being collected about students and student data privacy, as the owners of the tools rather than the educational institutions become the owners of the data [59].”
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    Premisecitation

    Challenges to the implementation of AI competencies include a lack of standardization of AI definitions, a lack of faculty expertise, and the absence of accreditation guidelines on AI in medical education.

    “Challenges to the implementation of AI competencies include a lack of standardization of AI definitions, a lack of faculty expertise, and the absence of accreditation guidelines on AI in medical education [7].”
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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Weidener et al evaluated German medical students and concluded that students must possess fundamental knowledge of AI systems, the ability to interpret AI input and output, and comprehension of the appropriate application of AI systems.

    “Weidener et al [2] evaluated German medical students and concluded that students must possess fundamental knowledge of AI systems, the ability to interpret AI input and output, and comprehension of the appropriate application of AI systems.”
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    Premisecitation

    AI cannot supplant the humanistic side of medicine.

    “emphasizing the humanistic side of medicine that AI cannot supplant [18].”
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    Premisecitation

    The AMA has played a pivotal leadership role in promoting significant innovation in medical education through its Accelerating Change in Medical Education Grant program and has built a national consortium-the AMA ChangeMedEd Consortium.

    “In addition to promoting grassroots advocacy from medical students through specific resolutions, in the last decade, the AMA has played a pivotal leadership role in promoting significant innovation in medical education through its Accelerating Change in Medical Education Grant program and has built a national consortium—the AMA ChangeMedEd Consortium [52].”
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    Interpretationreasoning

    Instant feedback such as that provided by the NYU pilot provides the mechanism and the accelerant to clear one of the remaining hurdles to competency-based education, appropriate evaluation, and assessment.

    “Instant feedback such as this provides the mechanism and the accelerant to clear one of the remaining hurdles to competency-based education, appropriate evaluation, and assessment.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Overreliance on AI could compromise the thoroughness of critical thinking and leave students ill-prepared for situations where AI is unavailable or inaccurately applied.

    “Furthermore, despite its capacity to propose differential diagnoses and management plans, overreliance on AI could compromise the thoroughness of critical thinking and leave students ill-prepared for situations where AI is unavailable or inaccurately applied [55-57].”
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    Premisecitation

    The inherent biases within the databases used for training large language models have the potential to perpetuate and even exacerbate existing health care inequities and, when used in the educational context, risk introducing skewed perspectives into clinical training.

    “The inherent biases within the databases used for training large language models have the potential to perpetuate and even exacerbate existing health care inequities and when used in the educational context, they risk introducing skewed perspectives into clinical training [58].”
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30 of 30

  • The paper relies on this without support.

    There are no papers, to our knowledge, that directly apply AI positions to the current UME frameworks of instruction.

    “While there are widespread calls to evaluate the potential implications of AI in medical practice and medical education [9,10,13-16], there are no papers, to our knowledge, that directly apply these positions to the current UME frameworks of instruction.”

  • The paper relies on this without support.

    The direct impact of AI on these EPAs remains largely unexplored.

    “Because the direct impact of AI on these EPAs remains largely unexplored, there is an urgent need to scrutinize these EPAs through the lens of AI-driven evolution.”

  • The paper relies on this without support.

    There is a lack of a deep learning system to identify norms and variances of safe patient care practices.

    “Similarly, there is a lack of a deep learning system to identify norms and variances of safe patient care practices.”

  • The paper relies on this without support.

    The integration of technology in health care is a current reality, not future speculation.

    “The integration of technology in health care is not future speculation, but a current reality.”

  • The paper relies on this without support.

    Current EPAs do not explicitly address the need for transdisciplinary team skills.

    “Current EPAs do not explicitly address the need for transdisciplinary team skills, yet the evolving nature of health care teams makes this a critical competency.”

  • The paper relies on this without support.

    The evolving nature of health care teams makes transdisciplinary team skills a critical competency.

    “Current EPAs do not explicitly address the need for transdisciplinary team skills, yet the evolving nature of health care teams makes this a critical competency.”

  • The paper relies on this without support.

    The rapid evolution and technological complexity of AI applications add to the difficulty of implementing AI competencies.

    “The rapid evolution and technological complexity of AI applications add to the difficulty of this task.”

  • The paper relies on this without support.

    A primary challenge in integrating AI into medical education is pedagogical adaptation to convey complex AI concepts to a predominantly clinical audience.

    “A primary challenge lies in the pedagogical adaptation required; educators must find ways to convey complex AI concepts to a predominantly clinical audience, ensuring that future physicians can both understand and critically evaluate the AI tools.”

  • The paper relies on this without support.

    The dynamic nature of AI can abruptly render current knowledge obsolete, demanding constant curriculum updates.

    “This is further complicated by the dynamic nature of AI, where rapid technological advancements can abruptly render current knowledge obsolete, demanding constant curriculum updates.”

  • The paper relies on this without support.

    Patient privacy is a paramount ethical concern because vast amounts of health data are collected and used in AI tools, often without patients being aware.

    “Furthermore, patient privacy is a paramount ethical concern as vast amounts of health data are collected and used in AI tools, often without the patients being aware.”
10 of 10

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References 58 · Cited by 33

Research works printed but not linked: 1

  1. BerkowitzCReports of the council on medical education: augmented intelligence in medical education (resolution 317-A-18)CME Report 4-A-1920192023-12-12Chicago, IllAmerican Medical Associationhttps://www.ama-assn.org/system/files/cme-report-4-a19-annotated.pdf

Authors
Jacobs, S. M., Lundy, N. N., Issenberg, S. B., & Chandran, L.
Affiliations
University of Miami (US)
Corresponding author
Issenberg, Saul Barry
ORCID
Venue
JMIR Medical Education
Publisher
JMIR Publications Inc.
Published
2023-12-19
Volume
9
Pages
e50903
ISSN
2369-3762 (ISSN-L)
DOI
10.2196/50903
PMC
PMC10762622
OpenAlex
W4389350630
Cited by
33
References
58
Citations by year
2026: 14 · 2025: 13 · 2024: 6
FWCI
8.42
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Education (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Leonard M. Miller School of Medicine
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › General Health Professions › Health and Medical Education
Keywords
entrustable professional activities, generative AI, undergraduate medical education, competency-based medical education, technological competence

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