• Richard A Young, Carmel M Martin, Joachim P Sturmberg, Sally Hall, Andrew Bazemore, Ioannis A Kakadiaris, and 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).
• J Am Board Fam Med. 2024 Mar 1; 37 (2): 332345332-345.

AbstractPrimary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.© Copyright 2024 by the American Board of Family Medicine.

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