Skip to main content

Main menu

  • Home
  • Current Issue
  • Content
    • Current Issue
    • Early Access
    • Multimedia
    • Podcast
    • Collections
    • Past Issues
    • Articles by Subject
    • Articles by Type
    • Supplements
    • Plain Language Summaries
    • Calls for Papers
  • Info for
    • Authors
    • Reviewers
    • Job Seekers
    • Media
  • About
    • Annals of Family Medicine
    • Editorial Staff & Boards
    • Sponsoring Organizations
    • Copyrights & Permissions
    • Announcements
  • Engage
    • Engage
    • e-Letters (Comments)
    • Subscribe
    • Podcast
    • E-mail Alerts
    • Journal Club
    • RSS
    • Annals Forum (Archive)
  • Contact
    • Contact Us
  • Careers

User menu

  • My alerts

Search

  • Advanced search
Annals of Family Medicine
  • My alerts
Annals of Family Medicine

Advanced Search

  • Home
  • Current Issue
  • Content
    • Current Issue
    • Early Access
    • Multimedia
    • Podcast
    • Collections
    • Past Issues
    • Articles by Subject
    • Articles by Type
    • Supplements
    • Plain Language Summaries
    • Calls for Papers
  • Info for
    • Authors
    • Reviewers
    • Job Seekers
    • Media
  • About
    • Annals of Family Medicine
    • Editorial Staff & Boards
    • Sponsoring Organizations
    • Copyrights & Permissions
    • Announcements
  • Engage
    • Engage
    • e-Letters (Comments)
    • Subscribe
    • Podcast
    • E-mail Alerts
    • Journal Club
    • RSS
    • Annals Forum (Archive)
  • Contact
    • Contact Us
  • Careers
  • Follow annalsfm on Twitter
  • Visit annalsfm on Facebook
DiscussionSpecial Reports

Competencies for the Use of Artificial Intelligence in Primary Care

Winston Liaw, Jacqueline K. Kueper, Steven Lin, Andrew Bazemore and Ioannis Kakadiaris
The Annals of Family Medicine November 2022, 20 (6) 559-563; DOI: https://doi.org/10.1370/afm.2887
Winston Liaw
1Department of Health Systems and Population Health Sciences, University of Houston Tilman J. Fertitta Family College of Medicine, Houston, Texas
MD, MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: winstonrliaw@gmail.com
Jacqueline K. Kueper
2Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, Ontario, Canada
3Department of Computer Science, Western University Faculty of Science, Ontario, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Steven Lin
4Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, California
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew Bazemore
5Center for Professionalism and Value in Health Care, Washington, DC
MD, MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ioannis Kakadiaris
6Department of Computer Science, University of Houston, Houston, Texas
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • eLetters
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Tables

  • Additional Files
    • View popup
    Table 1.

    Proposed Competencies for the Use of AI-Based Tools in Primary Care Decision Making

    DomainBottom LineCompetencyHypothetical Scenario
    Foundational knowledgeWhat is this tool?Clinicians will explain the fundamentals of AI, how AI-based tools are created and evaluated, the critical regulatory and socio-legal issues of the AI-based tools, and the current and emerging roles of AI in health care.The FDA approved an AI tool that provides a differential diagnosis using photographs of skin conditions and medical history. It was developed using 16,000 cases and a convolutional neural network to output prediction scores across 400 skin diseases.
    Critical appraisalShould I use this tool?Clinicians will appraise the evidence behind AI-based tools and assess their appropriate uses via validated evaluation frameworks for health care AI.In a retrospective study, the AI tool was superior to primary care clinicians, for which use was associated with improved diagnoses for 1 in every 10 cases. A prospective study in a clinical setting has not been done yet.
    Medical decision makingWhen should I use this tool?Clinicians will identify the appropriate indications for and incorporate the outputs of AI-based tools into medical decision making such that effectiveness, value, equity, fairness, and justice are enhanced.You decide to use this AI tool to augment your diagnostic ability for skin conditions where the diagnosis is unclear. You use it to inform, not override, your decisions regarding treatment, biopsies, and referrals in a way that boosts accuracy, quality of care, and resource stewardship.
    Technical useHow do I use this tool?Clinicians will execute the tasks needed to operate AI-based tools in a manner that supports efficiency and builds mastery.You learn to take clinical photographs of skin conditions as required by the AI tool and generate a differential diagnosis using it. You do this seamlessly and efficiently during physical exams.
    Patient communicationHow should I communicate with patients regarding the use of the tool?Clinicians will communicate what the tool is and why it is being used, answer questions about privacy and confidentiality, and engage in shared decision making, in a manner that preserves or augments the clinician-patient relationship.You discuss with the patient why and how the tool is being used and answer questions regarding privacy, ultimately building trust and confidence.
    Unintended consequences (cross-cutting)What are the “side effects” of this tool?Clinicians will anticipate and recognize the potential adverse effects of AI-based tools and take appropriate actions to mitigate or address unintended consequences.Foundational knowledge: You recognize that a convolutional neural network is a “black box.” As a result, you will not consult the tool for a rationale behind the suggested diagnosis. You remind yourself to guard against cognitive biases that may arise from only seeing the final suggested diagnosis.
    Critical appraisal: You understand that Fitzpatrick skin types I and V are under-represented, and type VI is absent in the data set for this AI tool.a
    Medical decision making: You anticipate that the tool will be less accurate for patients with these skin types and adjust your utilization, choosing to learn more about patients with these skin types.
    Technical use: You take the appropriate steps when the tool delivers an error message.
    Patient communication: You explain to the patient why your diagnosis is not the same as the one suggested by the tool, engaging in a shared decision making process that engenders trust, confidence, and respect.
    • AI = artificial intelligence; FDA = Food and Drug Administration.

    • Note: Fitzpatrick skin type 1 is pale white skin, while type VI is dark brown or black.

    • a The Fitzpatrick skin type classifies skin according to the amount of melanin pigment in the skin.

    • View popup
    Table 2.

    Proposed Artificial Intelligence Competencies by Learner Roles

    DomainMedical StudentsResidents
    All Prior Competencies +
    Faculty
    All Prior Competencies +
    Foundational knowledgeExplain the fundamentals of AI and how AI-based tools are created and evaluatedExplain the critical regulatory and socio-legal issues surrounding AI-based tools as they relate to practiceExplain, teach, and shape the current and emerging roles of AI in health care
    Critical appraisalDescribe the validated evaluation frameworks for AI-based tools in health careAppraise the evidence behind AI-based toolsExplain, teach, and contribute to the critical appraisal of AI-based tools
    Medical decision makingIdentify the appropriate indications for AI-based toolsIncorporate the outputs of AI-based tools into medical decision makingModel and study the use of AI in medical decision making to enhance effectiveness, value, and equity
    Technical useIdentify the tasks needed to operate AI-based toolsExecute the tasks needed to operate AI-based tools in a manner that builds efficiency and masteryTeach others to operate AI-based tools and execute the appropriate steps when these tools fail
    Patient communicationDescribe features of effective communication with patients regarding AI-based tools, including the rationale for their use, privacy, confidentiality, and shared decision makingDemonstrate effective communication regarding AI-based tools in simulated or real-world settingsModel effective communication in a manner that preserves or augments the clinician-patient relationship
    Unintended consequences (cross-cutting)Recognize the potential adverse effects of AI-based tools because of deficiencies in foundational knowledge, critical appraisal, medical decision making, technical use, and patient communicationTake appropriate actions to address potential adverse effects of AI-based toolsAnticipate and study the potential adverse effects of AI-based tools and model appropriate actions to mitigate them
    • AI = artificial intelligence.

Additional Files

  • Tables
  • VISUAL ABSTRACT IN PNG FILE BELOW

    • Liaw_visualabstract_20.6_v003.png
PreviousNext
Back to top

In this issue

Annals of Family Medicine: 20 (6)
Annals of Family Medicine: 20 (6)
Vol. 20, Issue 6
November/December 2022
  • Table of Contents
  • Index by author
  • Plain-language article summaries
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Annals of Family Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Competencies for the Use of Artificial Intelligence in Primary Care
(Your Name) has sent you a message from Annals of Family Medicine
(Your Name) thought you would like to see the Annals of Family Medicine web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
10 + 5 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Competencies for the Use of Artificial Intelligence in Primary Care
Winston Liaw, Jacqueline K. Kueper, Steven Lin, Andrew Bazemore, Ioannis Kakadiaris
The Annals of Family Medicine Nov 2022, 20 (6) 559-563; DOI: 10.1370/afm.2887

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Get Permissions
Share
Competencies for the Use of Artificial Intelligence in Primary Care
Winston Liaw, Jacqueline K. Kueper, Steven Lin, Andrew Bazemore, Ioannis Kakadiaris
The Annals of Family Medicine Nov 2022, 20 (6) 559-563; DOI: 10.1370/afm.2887
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • INTRODUCTION
    • Footnotes
    • REFERENCES
  • Figures & Data
  • eLetters
  • Info & Metrics
  • PDF

Related Articles

  • PubMed
  • Google Scholar

Cited By...

  • Bridging the Gap: Transforming Primary Care Through the Artificial Intelligence and Machine Learning for Primary Care (AIM-PC) Curriculum
  • Exploring Artificial Intelligence and the Future of Primary Care
  • Google Scholar

More in this TOC Section

  • Improving Early Detection of Cognitive Impairment in Older Adults in Primary Care Clinics: Recommendations From an Interdisciplinary Geriatrics Summit
  • Diabetes Management: A Case Study to Drive National Policy Change in Primary Care Settings
  • Family Medicine in Times of War
Show more Special Reports

Similar Articles

Subjects

  • Other topics:
    • Education
    • Health informatics
    • Communication / decision making

Keywords

  • artificial intelligence
  • primary care education
  • AI training
  • domains of competency

Content

  • Current Issue
  • Past Issues
  • Early Access
  • Plain-Language Summaries
  • Multimedia
  • Podcast
  • Articles by Type
  • Articles by Subject
  • Supplements
  • Calls for Papers

Info for

  • Authors
  • Reviewers
  • Job Seekers
  • Media

Engage

  • E-mail Alerts
  • e-Letters (Comments)
  • RSS
  • Journal Club
  • Submit a Manuscript
  • Subscribe
  • Family Medicine Careers

About

  • About Us
  • Editorial Board & Staff
  • Sponsoring Organizations
  • Copyrights & Permissions
  • Contact Us
  • eLetter/Comments Policy

© 2025 Annals of Family Medicine