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Cited by 166Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum

Open access
Grunhut, Joel, Marques, Oge, Wyatt, Adam T M·2022·JMIR Medical Education
DOI 10.2196/35587PMC PMC9214616

Published version of a preprint

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Abstract

Artificial intelligence (AI) is on course to become a mainstay in the patient’s room, physician’s office, and the surgical suite. Current advancements in health care technology might put future physicians in an insufficiently equipped position to deal with the advancements and challenges brought about by AI and machine learning solutions. Physicians will be tasked regularly with clinical decision-making with the assistance of AI-driven predictions. Present-day physicians are not trained to incorporate the suggestions of such predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their practice and evolving standards of care. Medical schools do not currently incorporate AI in their curriculum due to several factors, including the lack of faculty expertise, the lack of evidence to support the growing desire by students to learn about AI, or the lack of Liaison Committee on Medical Education’s guidance on AI in medical education. Medical schools should incorporate AI in the curriculum as a longitudinal thread in current subjects. Current students should understand the breadth of AI tools, the framework of engineering and designing AI solutions to clinical issues, and the role of data in the development of AI innovations. Study cases in the curriculum should include an AI recommendation that may present critical decision-making challenges. Finally, the ethical implications of AI in medicine must be at the forefront of any comprehensive medical education.

AI snapshot

Written from the paper’s full text.

TL;DR

The paper argues that undergraduate medical education must integrate artificial intelligence as a longitudinal curricular thread so future physicians can use, interpret, and ethically critique AI-driven clinical predictions. It outlines barriers and multitiered solutions at national, school, and student levels, calling for multi-institutional research and accreditation guidance.

Key findings

  • Medical schools do not currently incorporate AI in their curriculum due to factors including lack of faculty expertise, lack of evidence supporting students’ growing desire to learn AI, and lack of Liaison Committee on Medical Education guidance on AI in medical education.
  • The authors recommend AI be incorporated as a longitudinal thread in current subjects, covering the breadth of AI tools, engineering/design frameworks for clinical AI solutions, the role of data, case-based AI recommendations, and ethical implications.
  • Proposed solutions are multitiered: national infrastructure (AAMC questionnaire, materials, and guidance; LCME minimal requirements and expert panels; multi-institutional research), individual schools (faculty expansion, basic knowledge lessons, case-based learning), and students (journal clubs/reading groups, clinical AI exposure such as tumor board, radiology rotation, and point-of-care ultrasound).
  • The paper suggests adding 1-3 questions to the annual Association of American Medical Colleges Graduation Questionnaire to gauge student interest and ability over time, which could translate into accreditation requirements; it also notes the 2018 American Medical Association call for research on AI instruction and that three years later UME had been slow to address it.

Methods

  • Design: non-empirical commentary/perspective proposing curricular changes and solutions; no systematic review or primary data collection is described.
  • Data or sample: Not stated in the paper; no participants, institutions, or datasets are analyzed.
  • Main analysis: narrative synthesis of cited literature and a proposed multitiered solution table (Table 1), plus a methodological proposal for future long-term, multi-institutional educational research measuring student and faculty attitudes, skill level, and needs.

Limitations

  • Stated in the paper: the state of AI in medical education is in its infancy and speculative stages; few have given thought to the steps needed to create change; curricular reform is difficult due to resistance, lack of consensus, an already busy curriculum, deeply embedded values, the accreditation process, and faculty who may not be equipped to teach AI.
  • Evident from the text: there is no empirical evaluation or outcome data, the recommendations are not tested, and the argument relies on prior review and cited literature rather than a formal limitations section.

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

Integrity signals

Registry

Crossref · 10.2196/35587

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 25 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    The path to significant curricular reform is difficult because of deeply embedded values and the accreditation process.

    “The path to significant curricular reform is difficult because of a variety of factors, including deeply embedded values and the accreditation process [10].”
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • Interpretationcitation

    In order to be resilient to market disruptions from emerging technologies, physician training programs will require change.

    “In order to be resilient to these market disruptions, physician training programs will require change [9].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
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    • Rests on an assumption: Emerging technologies such as AI have the potential to disrupt labor markets maintained through traditional education programs.
  • Premisecitation

    Currently, the state of AI in medical education is in its infancy and speculative stages.

    “Currently, the state of AI in medical education is in its infancy and speculative stages [13-16].”
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  • Premisecitation

    Every specialty in medicine is exploring the use of AI in assisting physicians.

    “Recent data show that every specialty in medicine is exploring the use of AI in assisting physicians [4].”
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  • Premisecitation

    AI is met with resistance from physicians ill-equipped for such an evolution of clinical practice.

    “AI is met with resistance from physicians ill-equipped for such an evolution of clinical practice [3].”
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  • May combine two claims. 8
  • Classified as probably not a substantial claim. 19

  • May combine two claims.

    Findingcitation

    The majority of published literature on AI in undergraduate medical education calls for change in undergraduate medical education and research is necessary to support curricular changes.

    “Previously, we have shown that the majority of published literature on the topic call for change in undergraduate medical education and that research is necessary to support curricular changes [17].”
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • May combine two claims.

    Findingcitation

    Most of the current literature agrees that medical students do not need to learn how to code and create AI tools, but they should understand how AI works and its limitations.

    “Previously, we have shown that most of the current literature agrees that medical students do not need to learn how to code and create AI tools, but they should understand how AI works and its limitations [17].”
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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    Future advancements in AI are expected to permeate all aspects of medicine.

    “It is expected that the future advancements in AI will permeate all aspects of medicine [6].”
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    Premisecitation

    AI will continue to improve its capabilities to perform analysis and provide intelligent actionable recommendations on most forms of data.

    “AI will continue to improve its capabilities to perform analysis and provide intelligent actionable recommendations on most forms of data [5].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
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    Interpretationcitation

    AI will change the dynamics of the traditional clinician-patient dyad to a clinician-AI-patient triad, altering trust and accountability with ethical, legal, and financial implications.

    “AI will change the dynamics of the traditional clinician-patient dyad to a much more ethically and emotionally complex clinician-AI-patient triad. This will dramatically alter the trust and accountability aspects with ethical, legal, and financial implications [7].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [7] ChangAFrom the Moon and Formula One to Beethoven's Ninth Symphony: Musings of a Clinician-Data ScientistStanford University Center for Artificial Intelligence in Medicine & Imaging2022-05-31https://aimi.stanford.edu/events/aimi-invited-lecture-moon-and-formula-one-beethovens-ninth-symphony-musings-clinician-data

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

    Interpretationcitation

    Educating the next generation of physicians with the right techniques and adaptations to AI will enable them to become part of the emerging data science revolution.

    “Educating the next generation of physicians with the right techniques and adaptations to AI will enable them to become part of this emerging data science revolution [8].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] source

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  • May combine two claims.

    Premisecitation

    The practice of medicine is entering the age of AI in which the use of data to improve clinical decision-making will grow, bringing forth the need for skillful medicine-machine interaction.

    “The practice of medicine is entering the age of AI in which the use of data to improve clinical decision-making will grow, bringing forth the need for skillful medicine-machine interaction [11].”
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    The technological revolution raises ethical challenges for AI-based implementation in health care, including minority exclusions in databases, issues with legal protections, and a decrease in humanistic touch.

    “The technological revolution raises many challenges with regards to ethical considerations of AI-based implementation in health care. Minority exclusions in databases, issues with legal protections, and a decrease in humanistic touch, among other ethical issues, raise concern for an adaptation of AI in health care.”
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    • cites [12] source

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

    Premisecitation

    AI has the potential to solve the global doctor shortage and bring access to health care to remote areas of the world.

    “AI has the potential to solve the global doctor shortage and bring access to health care to remote areas of the world [2].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
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    Premisecitation

    Deep learning algorithms can make functional sense of increasing amounts of data used by individuals daily through wearables, smartphones, and other mobile monitoring sensors in different areas of medicine.

    “Deep learning algorithms can make functional sense of increasing amounts of data used by individuals daily through wearables, smartphones, and other mobile monitoring sensors in different areas of medicine [3].”
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  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Reading the current trends in AI in health care will inform and prepare students for the future of health care.

    “Reading the current trends in AI in health care will inform and prepare students for the future of health care.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    A joint and united research effort from multiple medical schools would provide a multifaceted and diverse input on the issue and is necessary.

    “A joint and united research effort from multiple medical schools would provide a multifaceted and diverse input on the issue and is necessary.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Few have given thought to the steps that must be taken to create change in AI medical education.

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

    Interpretationreasoning

    By integrating AI into cases and simulations, students can have exposure and familiarity with AI-based solutions.

    “By integrating AI into cases and simulations, students can have exposure and familiarity with AI-based solutions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    It is likely that medical schools will need to hire additional faculty in this field to ensure quality delivery of AI in medicine content.

    “It is likely that medical schools will need to hire additional faculty in this field to ensure quality delivery of AI in medicine content.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Medical school faculty may not be equipped to answer questions or discuss the role of AI in evidence-based medicine.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    When combined with evidence-based medicine learning at medical school, students will be able to assess the integrity of AI research.

    “When combined with evidence-based medicine learning at medical school, students will be able to assess the integrity of AI research.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Lessons in decision-making with AI will apply to individuals across the spectrum of health care, both in city and rural settings.

    “Lessons in decision-making with AI will apply to individuals across the spectrum of health care, both in city and rural settings.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    AI will permeate various health care settings because of its potential to expand access to health care.

    “It is expected that AI will permeate various health care settings because of its potential to expand access to health care.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Providing access to an AI in healthcare professional program will enable medical schools to infuse new knowledge in a curriculum that could not be provided otherwise.

    “Providing access to a program will enable medical schools to infuse new knowledge in a curriculum that could not be provided otherwise.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Through a longitudinal curricular thread in AI, medical schools can expose their students to a wide variety of specialties using AI.

    “Nevertheless, through a longitudinal curricular thread in AI, medical schools can expose their students to a wide variety of specialties using AI.”
    Quote in the paperNot a substantial claim

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

    Interpretationnone given

    Moving forward with collective agreement from medical education governing bodies, deans, and researchers will ensure current students receive adequate AI exposure.

    “Moving forward with collective agreement from these entities will ensure current students—our future physicians—receive adequate AI exposure.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    AI integration will be specialty specific, but AI as a whole will likely be present across health care.

    “AI integration will be specialty specific, but AI as a whole will likely be present across health care.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    Educational research is vital to assessing the effectiveness of different curricular reforms.

    “Educational research is vital to assessing the effectiveness of different curricular reforms [20].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
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  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Collaborating and managing AI applications will require a deep understanding of probabilistic reasoning and ethical consideration from medical students.

    “Collaborating and managing AI applications will require a deep understanding of probabilistic reasoning and ethical consideration from medical students [10].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
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23 of 23

  • The paper relies on this without support.

    Present-day physicians are not trained to incorporate AI predictions on a regular basis nor knowledgeable in an ethical approach to incorporating AI in their practice and evolving standards of care.

    “Present-day physicians are not trained to incorporate the suggestions of such predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their practice and evolving standards of care.”

  • The paper relies on this without support.

    Medical schools do not currently incorporate AI in their curriculum due to lack of faculty expertise, lack of evidence to support growing student desire to learn about AI, or lack of LCME guidance on AI in medical education.

    “Medical schools do not currently incorporate AI in their curriculum due to several factors, including the lack of faculty expertise, the lack of evidence to support the growing desire by students to learn about AI, or the lack of Liaison Committee on Medical Education’s guidance on AI in medical education.”

  • The paper relies on this without support.

    The current undergraduate medical education curriculum is increasingly out of sync with the new needs of an evolving technology.

    “The current undergraduate medical education (UME) curriculum is increasingly out of sync with the new needs of an evolving technology.”

  • The paper relies on this without support.

    A lack of knowledge in AI can create skepticism in the trustworthiness of a machine learning prediction.

    “Furthermore, a lack of knowledge in AI can create skepticism in the trustworthiness of a machine learning prediction.”

  • The paper relies on this without support.

    Emerging technologies such as AI have the potential to disrupt labor markets maintained through traditional education programs.

    “Emerging technologies such as AI have the potential to disrupt labor markets maintained through traditional education programs.”

  • The paper relies on this without support.

    Currently, only a small fraction of the total physician population is taking courses in data science, fellowship opportunities, or entering medical education programs from data science.

    “These represent a small fraction of the total physician population, and therefore a more integrative and forward approach in UME is necessary.”

  • The paper relies on this without support.

    Few have given thought to the steps that must be taken to create change in AI medical education.

    “Even so, few have given thought to the steps that must be taken to create this change.”

  • The paper relies on this without support.

    Medical school curricular changes are difficult to implement due to resistance to change.

    “Medical school curricular changes are difficult to implement due to resistance to change.”

  • The paper relies on this without support.

    This resistance is justified through a lack of consensus on how to incorporate change and an already busy curriculum.

    “This resistance is justified through a lack of consensus on how to incorporate change and an already busy curriculum.”

  • The paper relies on this without support.

    Proposing additional courses or workload will likely be met with criticism from medical education faculty in the best interest of medical students.

    “For this reason, proposing additional courses or workload will likely be met with criticism from medical education faculty in the best interest of medical students.”

  • The paper relies on this without support.

    Medical school faculty may not be equipped to answer questions or discuss the role of AI in evidence-based medicine.

    “Medical school faculty may not be equipped to answer questions or discuss the role of AI in evidence-based medicine.”

  • The paper relies on this without support.

    It is far too difficult to teach entire courses of AI in a medical school curriculum.

    “It is far too difficult to teach entire courses of AI in a medical school curriculum.”

  • The paper relies on this without support.

    In most schools, the instructor for an introductory AI lecture will likely be a data scientist.

    “In most schools, the instructor will likely be a data scientist, but it is important that the instructor has teaching experience, is familiar with medical students, and is conversant with the role of AI in medicine.”

  • The paper relies on this without support.

    AI research has a strong appearance in many leading journals, including medical AI-themed journals.

    “AI research has a strong appearance in many leading journals, including medical AI-themed journals.”

  • The paper relies on this without support.

    A tumor board, consisting of radiologists, pathologists, oncologists, and surgeons, is impacted by AI applications.

    “For example, a tumor board, consisting of radiologists, pathologists, oncologists, and surgeons, is impacted by AI applications.”

  • The paper relies on this without support.

    New medical curricular changes can have tremendous positive and negative impacts on medical students.

    “New medical curricular changes can have tremendous positive and negative impacts on medical students.”

  • The paper relies on this without support.

    A change in curriculum such as the introduction of an entire novel topic is a difficult task.

    “Additionally, a change in curriculum such as the introduction of an entire novel topic is a difficult task.”
17 of 17

Claims other papers make about this one

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Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Augmented Intelligence in Health CareAmerican Medical Association20182022-05-31https://www.ama-assn.org/system/files/2019-01/augmented-intelligence-policy-report.pdf

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Related

Authors
Grunhut, J., Marques, O., & Wyatt, A. T. M.
Affiliations
Florida Atlantic University (US)
Corresponding author
Grunhut, Joel
ORCID
Venue
JMIR Medical Education
Publisher
JMIR Publications Inc.
Published
2022-06-07
Volume
8
Issue
2
Pages
e35587
ISSN
2369-3762 (ISSN-L)
DOI
10.2196/35587
PMC
PMC9214616
OpenAlex
W4282916733
Semantic Scholar
b30893c704112e2d0f2eee18a0f7426a343c583d
Cited by
166
References
23
Citations by year
2026: 41 · 2025: 58 · 2024: 47 · 2023: 18 · 2022: 2
FWCI
9.41
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)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
artificial intelligence in healthcare, medical curriculum, clinical decision-making, AI ethics, Liaison Committee on Medical Education, faculty expertise, case-based learning

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