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Cited by 84Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationClinical Reasoning and Diagnostic Skills

Competencies for the Use of Artificial Intelligence in Primary Care

Open access
Liaw, Winston, Kueper, Jacqueline K., Lin, Steven, Bazemore, Andrew, Kakadiaris, Ioannis·2022·The Annals of Family Medicine
DOI 10.1370/afm.2887PMC PMC9705044
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Abstract

The artificial intelligence (AI) revolution has arrived for the health care sector and is finally penetrating the far-reaching but perpetually underfinanced primary care platform. While AI has the potential to facilitate the achievement of the Quintuple Aim (better patient outcomes, population health, and health equity at lower costs while preserving clinician well-being), inattention to primary care training in the use of AI-based tools risks the opposite effects, imposing harm and exacerbating inequalities. The impact of AI-based tools on these aims will depend heavily on the decisions and skills of primary care clinicians; therefore, appropriate medical education and training will be crucial to maximize potential benefits and minimize harms. To facilitate this training, we propose 6 domains of competency for the effective deployment of AI-based tools in primary care: (1) foundational knowledge (what is this tool?), (2) critical appraisal (should I use this tool?), (3) medical decision making (when should I use this tool?), (4) technical use (how do I use this tool?), (5) patient communication (how should I communicate with patients regarding the use of this tool?), and (6) awareness of unintended consequences (what are the "side effects" of this tool?). Integrating these competencies will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape. Nonetheless, even incremental increases in AI-relevant training may be beneficial, and the sooner these challenges are tackled, the sooner the primary care workforce and those served by it will begin to reap the benefits.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper proposes 6 domains of competency for using AI-based tools in primary care, arguing that without training, AI deployment risks harm, inequity, and clinician burnout rather than progress toward the Quintuple Aim. The competencies are foundational knowledge, critical appraisal, medical decision making, technical use, patient communication, and awareness of unintended consequences.

Key findings

As a proposal paper, it advances a framework rather than reporting empirical results:

  • Six competency domains are proposed, each framed by a question: foundational knowledge ("what is this tool?"), critical appraisal ("should I use this tool?"), medical decision making ("when should I use this tool?"), technical use ("how do I use this tool?"), patient communication ("how should I communicate with patients regarding the use of this tool?"), and unintended consequences ("what are the 'side effects' of this tool?"), the last being cross-cutting.
  • Competencies are mapped to learner roles (medical students, residents, faculty), with expectations escalating from describing/identifying, to appraising/executing, to teaching/modeling/studying.
  • The authors cite existing evidence of training gaps: in one national study in Ireland, two-thirds of medical students received no AI training and over 40% had never heard the term "machine learning"; in the US and abroad, 3 out of 4 medical students think AI competencies should be taught in medical school.
  • They note that a systematic review of EBM curricula found no studies assessing influence on patient outcomes, no validated assessment tool, and lack of teachers as a barrier — highlighting the need to adapt AI curricula to primary care and evaluate them (e.g., tracking effects on burnout and AI knowledge, skills, and attitudes).

Methods

Not stated in the paper (no empirical design, data, or analysis); this is a perspective/proposal article that synthesizes prior literature and existing initiatives to put forward a competency framework. The authors describe their approach as building on competencies published by others and illustrating each domain with a hypothetical dermatology AI scenario (a tool developed using 16,000 cases and a convolutional neural network outputting prediction scores across 400 skin diseases).

Limitations

The paper states that these competencies are "a point of departure" and that more work is needed before integrating them into training; subcompetencies for primary, intermediate, and expert users still need to be developed. It notes that the competencies will change over time and must be tailored to local context (disease prevalence, priority problems, community resources), and that integrating them is not straightforward given the breadth of existing family medicine training and the constantly changing technological landscape. Evident from the text: the framework is not yet validated or tested, and the authors acknowledge that only a small percentage of clinicians will become expert users.

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

Integrity signals

Registry

Crossref · 10.1370/afm.2887

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 41 cited sources checked · 0 supported · 0 flagged

  • Premisecitation

    AI has the potential to facilitate achievement of the Quintuple Aim (better patient outcomes, population health, and health equity at lower costs while preserving clinician wellbeing).

    “to advance the Quintuple Aim of better patient outcomes, population health, and health equity, at lower costs while preserving clinician well-being.9”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [9]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Interpretationcitation

    Medical education has been slow to adapt.

    “Unfortunately, medical education has been slow to adapt.”
    Quote in the paperSubstantial claim
  • Premisecitation

    In one national study in Ireland, over 40% of medical students had never heard of the term 'machine learning.'

    “and over 40% had never heard of the term”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [25]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Premisecitation

    In the United States and abroad, 3 out of 4 medical students think AI competencies should be taught in medical school.

    “In the United States and abroad, 3 out of 4 medical students think AI competencies should be taught in medical school.24,25”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [24]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [25]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Premisecitation

    One systematic review on EBM curricula found that a lack of EBM teachers is a barrier to broader dissemination of such curricula.

    “one systematic review on EBM curricula found that no studies assessed the influence of these curricula on patient outcomes, that there was no validated tool to assess these curricula, and that a lack of EBM teachers is a barrier to broader dissemination of such curricula.42,43”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [42]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [43]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. 20
  • May combine two claims. 9

  • Probably not a substantial claim: may not pass S1 (asserted).

    Premisecitation

    AI can magnify existing biases.

    “critics argue that AI can magnify existing biases, is not generalizable, and degrades over time.[5][6][7][8]”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [5]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [7]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [8]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationreasoning

    Inattention to primary care training in the use of AI-based tools risks imposing harm and exacerbating inequalities.

    “inattention to primary care training in the use of AI-based tools risks the opposite effects, imposing harm and exacerbating inequalities.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    The impact of AI-based tools on the Quintuple Aim will depend heavily on the decisions and skills of primary care clinicians.

    “The impact of AI-based tools on these aims will depend heavily on the decisions and skills of primary care clinicians”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Integrating the proposed AI competencies into family medicine training will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape.

    “Integrating these competencies will not be straightforward because of the breadth of knowledge already incorporated into family medicine training and the constantly changing technological landscape.”
    Quote in the paperNot a substantial claim

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

    Interpretationnone given

    Even incremental increases in AI-relevant training may be beneficial.

    “Nonetheless, even incremental increases in AI-relevant training may be beneficial”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).

    Premisecitation

    AI is not generalizable.

    “critics argue that AI can magnify existing biases, is not generalizable, and degrades over time.[5][6][7][8]”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [5]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [7]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [8]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationreasoning

    If AI-based tools prove beneficial, their adoption has important implications for equity.

    “If these tools prove beneficial, their adoption has important implications for equity.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Clinicians are calling for more training because they feel ill-prepared to thrive within this digital future.

    “clinicians are calling for more training because they feel ill-prepared to thrive within this digital future.23”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [23]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Premisecitation

    Primary care clinicians need additional knowledge and skills so that AI can support the primary care functions of continuity, coordination, timeliness, and comprehensiveness.

    “our workforce needs additional knowledge and skills so that AI can support the primary care functions of continuity, coordination, timeliness, and comprehensiveness.3,10,11”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [3]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [10]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [11]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Premisecitation

    Following the expansion of virtual care during the COVID-19 pandemic, professional societies have published competencies to guide telehealth training.

    “Following the expansion of virtual care during the COVID-19 pandemic, professional societies have published competencies to guide telehealth training.19,20”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [19]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [20]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationreasoning

    Ultimately, only a small percentage of primary care clinicians will become expert users.

    “Ultimately, only a small percentage of primary care clinicians will become expert users”
    Quote in the paperNot a substantial claim

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

    Interpretationcitation

    These findings highlight the need to adapt AI curricula to primary care.

    “These findings highlight the need to adapt AI curricula to primary care.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Without training, the goal of using AI to advance the Quintuple Aim will not be achieved, risking harm instead of intended benefits.

    “Without training, this goal will not be achieved, risking harm instead of the intended benefits.”
    Quote in the paperNot a substantial claim

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

    Interpretationcitation

    AI will become more widespread, with tools progressing from the development to the evaluation, validation, and monitoring phases.

    “we anticipate that AI will become more widespread, with tools progressing from the development to the evaluation, validation, and monitoring phases.36”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    Patients in resource-poor communities may lack access to the requisite technology, which can exacerbate disparities similar to how telehealth use varied during the COVID-19 pandemic.

    “Patients in resource-poor communities may lack access to the requisite technology, 31 which can exacerbate disparities similar to how telehealth use varied during the COVID-19 pandemic.32”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [31]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [32]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationreasoning

    Patient safety will be compromised, and clinicians will become dissatisfied, leading to higher costs, greater fragmentation, and more burnout.

    “Patient safety will be compromised, and clinicians will become dissatisfied, leading to higher costs, greater fragmentation, and more burnout.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI-based tools will be deployed without rigorous evaluation and created absent specification for the unique needs of primary care.

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

    Interpretationreasoning

    These competencies will change over time and must be tailored to the local context.

    “Second, these competencies will change over time and must be tailored to the local context.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    Primary care is the largest health care delivery platform in the United States.

    “Training in AI is essential for primary care, the United State's largest health care delivery platform.12”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationnone given

    The specific AI-based tools presented to students may differ based on the prevalence of diseases, the high-priority problems, and the resources available within communities.

    “the specific AI-based tools presented to students may differ based on the prevalence of diseases, the high-priority problems, and the resources available within communities.”
    Quote in the paperNot a substantial claim

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

    Premisecitation

    Incorporating EBM into primary care required customization, with primary care educators emphasizing the importance of patient-oriented evidence, information mastery, and primary care research methods.

    “Incorporating EBM into primary care required customization, with primary care educators emphasizing the importance of patient-oriented evidence, information mastery, and primary care research methods.41”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [41]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).

    Premisecitation

    AI degrades over time.

    “critics argue that AI can magnify existing biases, is not generalizable, and degrades over time.[5][6][7][8]”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [5]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [6]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [7]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [8]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    Interpreting poorly organized data streams in primary care is demanding and a source of burnout.

    “Interpreting these poorly organized data streams is demanding and a source of burnout.13”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [13]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

22 of 22

  • The paper relies on this without support.

    AI-based tools will be deployed without rigorous evaluation and created absent specification for the unique needs of primary care.

    “AI-based tools will be deployed without rigorous evaluation and created absent specification for the unique needs of primary care.”

  • The paper relies on this without support.

    Little has been done to operationalize requirements that face important revisions in 2022.

    “Little has been done, however, to operationalize requirements that face important revisions in 2022.18”

  • The paper relies on this without support.

    Some AI approaches (such as neural networks) prioritize accuracy over explainability.

    “Similarly, some AI approaches (such as neural networks) prioritize accuracy over explainability.”

  • The paper relies on this without support.

    The technology needed to use AI-based tools will inevitably fail.

    “Furthermore, the technology needed to use AI-based tools will inevitably fail.”

  • The paper relies on this without support.

    The application of any technology comes with unintended consequences.

    “Lastly, the application of any technology comes with unintended consequences.”

  • The paper relies on this without support.

    Some professional societies, such as ABAIM and AMIA, have foundational curricula that can be adapted for primary care.

    “Some professional societies, such as ABAIM and the American Medical Informatics Association (AMIA), have foundational curricula that can be adapted for primary care.”
6 of 6

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. ANNALS OF FAMILY MEDICINE ✦ WWW.ANNFAMMED.ORG ✦ VOL. 20, NO. 6 ✦ NOVEMBER/DECEMBER 2022

Cited by

Sorted by citations

Related

Authors
Liaw, W., Kueper, J. K., Lin, S., Bazemore, A., & Kakadiaris, I.
Affiliations
  • University of Houston (US)
  • Western University (CA)
  • Stanford Health Care (US)
ORCID
Venue
The Annals of Family Medicine
Publisher
Annals of Family Medicine
Published
2022-11
Volume
20
Issue
6
Pages
559-563
ISSN
  • 1544-1709 (ISSN-L)
  • 1544-1717
DOI
10.1370/afm.2887
PMC
PMC9705044
OpenAlex
W4310460542
Semantic Scholar
f91034c0771a3517968fc6428f23ee04cccc236d
Cited by
83
References
32
Citations by year
2026: 27 · 2025: 33 · 2024: 15 · 2023: 8
FWCI
3.05
Type
journal article
Language
en
Open access
Locations
  • The Annals of Family Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (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
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Keywords
artificial intelligence in healthcare, primary care, clinician competence, medical education, Quintuple Aim, clinical decision-making, patient communication, unintended consequences of AI, family medicine education, health equity
MeSH terms
Clinical Decision-Making, Artificial Intelligence, Communication, Humans, Primary Health Care, Technology

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-23.

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