Skip to main content

Main menu

  • HOME
  • ARTICLES
    • Current Issue
    • Abstracts In Press
    • Archives
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • Other Publications
    • abfm

User menu

Search

  • Advanced search
American Board of Family Medicine
  • Other Publications
    • abfm
American Board of Family Medicine

American Board of Family Medicine

Advanced Search

  • HOME
  • ARTICLES
    • Current Issue
    • Abstracts In Press
    • Archives
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • JABFM on Bluesky
  • JABFM On Facebook
  • JABFM On Twitter
  • JABFM On YouTube
Research ArticleSpecial Communication

A Clinician's Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution

Steven Lin
The Journal of the American Board of Family Medicine January 2022, 35 (1) 175-184; DOI: https://doi.org/10.3122/jabfm.2022.01.210226
Steven Lin
From Stanford 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
  • Article
  • Figures & Data
  • References
  • Info & Metrics
  • PDF
Loading

References

  1. 1.↵
    1. Esteva A,
    2. Robicquet A,
    3. Ramsundar B,
    4. et al
    . A guide to deep learning in healthcare. Nat Med 2019;25:24–9.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. DeSilver D
    [Internet]. Chart of the week: the ever-accelerating rate of technology adoption. Pew Research Center website; 2014 [cited 2021 March 19]. Available from: https://www.pewresearch.org/fact-tank/2014/03/14/chart-of-the-week-the-ever-accelerating-rate-of-technology-adoption/.
  3. 3.↵
    1. Perrault R,
    2. Shoham Y,
    3. Brynjolfsson E,
    4. et al
    . [Internet]. The AI Index 2019 Annual Report. Stanford University Human-Centered AI Institute website; 2019 [cited 2021 March 19]. Available from: https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf.
  4. 4.↵
    1. Kasparov G
    . Chess, a Drosophila of reasoning. Science 2018;362:1087.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Silver D,
    2. Hubert T,
    3. Schrittwieser J,
    4. et al
    . A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 2018;362:1140–4.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. Jiang HJ,
    2. Russo CA,
    3. Barrett ML
    [Internet]. Nationwide frequency and costs of potentially preventable hospitalizations, 2006. Healthcare Cost and Utilization Project Statistical Brief #72. U.S. Agency for Healthcare Research and Quality, Rockville, MD; 2009 [cited 2021 March 19]. Available from: https://www.hcup-us.ahrq.gov/reports/statbriefs/sb72.pdf.
  7. 7.↵
    1. Morgenstern JD,
    2. Buajitti E,
    3. O'Neill M,
    4. et al
    . Predicting population health with machine learning: a scoping review. BMJ Open 2020;10:e037860.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Yarnall K,
    2. Pollak KI,
    3. Ostbye T,
    4. Krause KM,
    5. Michener JL
    . Primary care: is there enough time for prevention? Am J Public Health 2003;93:635–41.
    OpenUrlCrossRefPubMedWeb of Science
  9. 9.↵
    1. Winn AN,
    2. Somai M,
    3. Fergestrom N,
    4. Crotty BH
    . Association of use of online symptom checkers with patients' plans for seeking care. JAMA Netw Open 2019;2:e1918561.
    OpenUrl
  10. 10.↵
    1. Rajkomar A,
    2. Yim J,
    3. Grumbach K,
    4. Parekh A
    . Weighting primary care patient panel size: a novel electronic health record-derived measure using machine learning. JMIR Med Inform 2016;4:e29.
    OpenUrl
  11. 11.↵
    1. McCarthy J
    [Internet]. One in five U.S. adults use health apps, wearable trackers. Gallup website; 2019 [cited 2021 March 19]. Available from: https://news.gallup.com/poll/269096/one-five-adults-health-apps-wearable-trackers.aspx.
  12. 12.↵
    1. Stein N,
    2. Brooks K
    . A fully automated conversational artificial intelligence for weight loss: longitudinal observational study among overweight and obese adults. JMIR Diabetes 2017;2:e28.
    OpenUrl
  13. 13.↵
    1. Sinsky C,
    2. Colligan L,
    3. Li L,
    4. et al
    . Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med 2016;165:753–60.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Lin S
    . The present and future of team documentation: the role of patients, families, and artificial intelligence. Mayo Clin Proc 2020;95:852–5.
    OpenUrl
  15. 15.↵
    1. Smith SV
    [Internet]. How a machine learned to spot depression. National Public Radio website; 2015 [cited 2021 March 19]. Available from: https://www.npr.org/sections/money/2015/05/20/407978049/how-a-machine-learned-to-spot-depression.
  16. 16.↵
    1. Savoy M
    . IDx-DR for diabetic retinopathy screening. Am Fam Physician 2020;101:307–8.
    OpenUrl
  17. 17.↵
    1. Liu Y,
    2. Jain A,
    3. Eng C,
    4. et al
    . A deep learning system for differential diagnosis of skin diseases. Nat Med 2020;26:900–8.
    OpenUrlCrossRef
  18. 18.↵
    1. Lin S,
    2. Shanafelt TD,
    3. Asch SM
    . Reimagining clinical documentation with artificial intelligence. Mayo Clin Proc 2018;93:563–5.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. Rabinowitz S,
    2. Iroanya E
    [Internet]. Can machine learning reduce patient, provider and insurer administrative burdens? Healthcare Information and Management Systems Society website; 2019 [cited 2021 March 19]. Available from: https://www.himss.org/resources/can-machine-learning-reduce-patient-provider-and-insurer-administrative-burdens.
  20. 20.↵
    1. Holdsworth L,
    2. Park C,
    3. Asch SM,
    4. Lin S
    . The potential use of technology-enabled and artificial intelligence support for pre-visit planning in ambulatory care: findings from an environmental scan. Ann Fam Med 2021;19:419–26.
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    1. Lin S,
    2. Sattler A,
    3. Smith M
    . Retooling primary care in the COVID-19 era. Mayo Clin Proc 2020;95:1831–4.
    OpenUrlPubMed
  22. 22.↵
    1. Buolamwini J,
    2. Gebru T
    . Gender shades: intersectional accuracy disparities in commercial gender classification. PMLR 2018;81:77–91.
    OpenUrl
  23. 23.↵
    1. Fowler GA
    [Internet]. Black Lives Matter could change facial recognition forever—if Big Tech doesn't stand in the way. Washington Post; 2020 [cited 2021 March 19]. Available from: https://www.washingtonpost.com/technology/2020/06/12/facial-recognition-ban/.
  24. 24.↵
    1. Coley RY,
    2. Johnson E,
    3. Simon GE,
    4. Cruz M,
    5. Shortreed SM
    . Racial/ethnic disparities in the performance of prediction models for death by suicide after mental health visits. JAMA Psychiatry 2021;78:726–34.
    OpenUrl
  25. 25.↵
    1. Obermeyer Z,
    2. Powers B,
    3. Vogeli C,
    4. Mullainathan S
    . Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019;366:447–53.
    OpenUrlAbstract/FREE Full Text
  26. 26.↵
    1. Hammond G,
    2. Johnston K,
    3. Huang K,
    4. Joynt Maddox KE
    . Social determinants of health improve predictive accuracy of clinical risk models for cardiovascular hospitalization, annual cost, and death. Circ Cardiovasc Qual Outcomes 2020;13:e006752.
    OpenUrl
  27. 27.↵
    Unfairness by algorithm: distilling the harms of automated decision-making [Internet]. Future of Privacy Forum, Washington, DC; 2017 [cited 2021 March 19]. Available from: https://fpf.org/wp-content/uploads/2017/12/FPF-Automated-Decision-Making-Harms-and-Mitigation-Charts.pdf.
  28. 28.↵
    Gender Shades website [Internet]. MIT Media Lab; 2018 [cited 2021 March 19]. Available from: http://gendershades.org/.
  29. 29.↵
    1. Wong A,
    2. Otles E,
    3. Donnelly JP,
    4. et al
    . External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med 2021;181:1065–70.
    OpenUrl
  30. 30.↵
    1. Kashyap M,
    2. Seneviratne M,
    3. Banda JM,
    4. et al
    . Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network. J Am Med Inform Assoc 2020;27:877–83.
    OpenUrl
  31. 31.↵
    1. Davis SE,
    2. Lasko TA,
    3. Chen G,
    4. Matheny ME
    . Calibration drift among regression and machine learning models for hospital mortality. AMIA Annu Symp Proc 2017;2017:625–34.
    OpenUrlPubMed
  32. 32.↵
    The Social Dilemma website [Internet]; 2020 [cited 2021 March 19]. Available from: https://www.thesocialdilemma.com/.
  33. 33.↵
    Ledger of Harms [Internet]. Center for Humane Technology website; 2020 [cited 2021 March 19]. Available from: https://ledger.humanetech.com/.
  34. 34.↵
    U.S. Capitol Riot [Internet]. New York Times; 2021 [cited 2021 March 19]. Available from: https://www.nytimes.com/spotlight/us-capitol-riots-investigations.
  35. 35.↵
    1. Vosoughi S,
    2. Roy D,
    3. Aral S
    . The spread of true and false news online. Science 2018;359:1146–51.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Dwoskin E
    [Internet]. Misinformation on Facebook got six times more clicks than factual news during the 2020 election, study says. Wall Street Journal; 2021 [cited 2021 Sept 6]. Available from: https://www.washingtonpost.com/technology/2021/09/03/facebook-misinformation-nyu-study/.
  37. 37.↵
    1. Taneja H
    [Internet]. The era of “move fast and break things” is over. Harvard Business Review website; 2019 [cited 2021 March 19]. Available from: https://hbr.org/2019/01/the-era-of-move-fast-and-break-things-is-over.
  38. 38.↵
    1. Lin S,
    2. Mahoney MR,
    3. Sinsky CA
    . Ten ways artificial intelligence will transform primary care. J Gen Intern Med 2019;34:1626–30.
    OpenUrl
  39. 39.↵
    1. Starfield B
    . Primary care: concept, evaluation, and policy. New York: Oxford University Press; 1992.
  40. 40.↵
    1. Stange KC
    . Barbara Starfield: passage of the pathfinder of primary care. Ann Fam Med 2011;9:292–6.
    OpenUrlFREE Full Text
  41. 41.↵
    Stanford Healthcare AI Applied Research Team [Internet]. Stanford Medicine website; 2021 [cited 2021 March 19]. Available from: https://med.stanford.edu/healthcare-ai.
  42. 42.↵
    1. Smith M,
    2. Sattler A,
    3. Hong G,
    4. Lin S
    . From code to bedside: implementing artificial intelligence using quality improvement methods. J Gen Intern Med 2021;36:1061–6.
    OpenUrl
  43. 43.↵
    1. Matheny M,
    2. Thadaney Israni S,
    3. Ahmed M,
    4. Whicher D
    . Editors. 2019. Artificial intelligence in health care: the hope, the hype, the promise, the peril. Washington (DC): National Academy of Medicine.
  44. 44.↵
    1. Eubanks V
    . Automating inequality: how high-tech tools profile, police, and punish the poor. New York: St. Martin's Press; 2018.
  45. 45.↵
    1. O'Neil C
    . Weapons of math destruction: how big data increases inequality and threatens democracy. New York: Crown Books; 2016.
  46. 46.↵
    Algorithmic Justice League website [Internet]; 2021 [cited 2021 March 19]. Available from: https://www.ajl.org/.
  47. 47.↵
    Center for Humane Technology website [Internet]; 2021 [cited 2021 March 19]. Available from: https://www.humanetech.com/.
  48. 48.↵
    Data for Black Lives website [Internet]; 2021 [cited 2021 March 19]. Available from: https://d4bl.org/.
  49. 49.↵
    American Medical Informatics Association website [Internet]; 2021 [cited 2021 Sept 6]. Available from: https://amia.org/.
  50. 50.↵
    American Board of Artificial Intelligence in Medicine website [Internet]; 2021 [cited 2021 Sept 6 19]. Available from: https://abaim.org/.
PreviousNext
Back to top

In this issue

The Journal of the American Board of Family     Medicine: 35 (1)
The Journal of the American Board of Family Medicine
Vol. 35, Issue 1
January/February 2022
  • Table of Contents
  • Table of Contents (PDF)
  • Cover (PDF)
  • Index by author
  • Back Matter (PDF)
  • Front Matter (PDF)
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 American Board 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.
A Clinician's Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution
(Your Name) has sent you a message from American Board of Family Medicine
(Your Name) thought you would like to see the American Board 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.
2 + 13 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
A Clinician's Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution
Steven Lin
The Journal of the American Board of Family Medicine Jan 2022, 35 (1) 175-184; DOI: 10.3122/jabfm.2022.01.210226

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
A Clinician's Guide to Artificial Intelligence (AI): Why and How Primary Care Should Lead the Health Care AI Revolution
Steven Lin
The Journal of the American Board of Family Medicine Jan 2022, 35 (1) 175-184; DOI: 10.3122/jabfm.2022.01.210226
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • A Primer on AI for Primary Care Providers
    • Ten Ways AI Is Transforming Health Care
    • Key Limitations and Ethical Pitfalls of AI
    • Why Primary Care Should Lead Health Care AI
    • How Primary Care Will Lead Health Care AI
    • Conclusion
    • Acknowledgments
    • Notes
    • References
  • Figures & Data
  • References
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Storylines of family medicine VIII: clinical approaches
  • Implications of conscious AI in primary healthcare
  • What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
  • Perceptions of Artificial Intelligence Use in Primary Care: A Qualitative Study with Providers and Staff of Ontario Community Health Centres
  • Perceptions of Artificial Intelligence Use in Primary Care: A Qualitative Study with Providers and Staff of Ontario Community Health Centres
  • Competencies for the Use of Artificial Intelligence in Primary Care
  • Health Care Equity for Family Medicine Patients and Family Physician Equity
  • Google Scholar

More in this TOC Section

  • In Defense of Generalists: Primary Care Observations Have Systematic Advantages
  • Looking Back to Move Forward: Reflections of PBRN Directors
  • Building a Primary Care Research Agenda for Latino Populations in the Setting of the Latino Paradox: A Report from the 2023 Latino Primary Care Summit
Show more Special Communication

Similar Articles

Keywords

  • Artificial Intelligence
  • Deep Learning
  • Delivery of Health Care
  • Health Equity
  • Information Technology
  • Machine Learning
  • Primary Health Care
  • Quality Improvement
  • Social Justice
  • Technology

Navigate

  • Home
  • Current Issue
  • Past Issues

Authors & Reviewers

  • Info For Authors
  • Info For Reviewers
  • Submit A Manuscript/Review

Other Services

  • Get Email Alerts
  • Classifieds
  • Reprints and Permissions

Other Resources

  • Forms
  • Contact Us
  • ABFM News

© 2025 American Board of Family Medicine

Powered by HighWire