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Research ArticleSpecial Communication

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care

Richard A. Young, Carmel M. Martin, Joachim P. Sturmberg, Sally Hall, Andrew Bazemore, Ioannis A. Kakadiaris and Steven Lin
The Journal of the American Board of Family Medicine March 2024, 37 (2) 332-345; DOI: https://doi.org/10.3122/jabfm.2023.230219R1
Richard A. Young
From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).
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Carmel M. Martin
From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).
MBBS, MSc, PhD, MRCGP, FRACGP, FAFPHM
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Joachim P. Sturmberg
From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).
MBBS, MFM, PhD, FRACGP
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Sally Hall
From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).
PhD
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Andrew Bazemore
From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).
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Ioannis A. Kakadiaris
From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).
PhD
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Steven Lin
From the Director of Research and Associate Program Director, JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Medicine, Nursing and Allied Health, Monash University, Melbourne, Victoria, Australia (CMM); School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan New South Wales, Australia (JPS); Australian National University College of Health and Medicine, Canberra, Australia (SH); American Board of Family Medicine, Lexington, KY (AB); University of Houston (IAK); Stanford University School of Medicine Stanford, CA (SL).
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The Journal of the American Board of Family     Medicine: 37 (2)
The Journal of the American Board of Family Medicine
Vol. 37, Issue 2
March-April 2024
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What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
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What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
Richard A. Young, Carmel M. Martin, Joachim P. Sturmberg, Sally Hall, Andrew Bazemore, Ioannis A. Kakadiaris, Steven Lin
The Journal of the American Board of Family Medicine Mar 2024, 37 (2) 332-345; DOI: 10.3122/jabfm.2023.230219R1

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What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care
Richard A. Young, Carmel M. Martin, Joachim P. Sturmberg, Sally Hall, Andrew Bazemore, Ioannis A. Kakadiaris, Steven Lin
The Journal of the American Board of Family Medicine Mar 2024, 37 (2) 332-345; DOI: 10.3122/jabfm.2023.230219R1
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  • Article
    • Abstract
    • Background
    • The Opportunities and Challenges of AI/ML for Primary Care
    • Data Issues—Signal, Noise, and Action
    • Other Concerns with AI/ML
    • Discussion
    • Acknowledgments
    • Appendix.
    • Notes
    • References
    • References
  • Figures & Data
  • References
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Keywords

  • Artificial Intelligence
  • Clinical Decision-Making
  • Complexity Science
  • Information Technology
  • Machine Learning
  • Medical Informatics
  • Primary Care Physicians
  • Primary Health Care
  • Quality Improvement

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