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Article CommentaryCommentary

Family Medicine Must Prepare for Artificial Intelligence

Karim Hanna, David Chartash, Winston Liaw, Damian Archer, Daniel Parente, Nipa R. Shah, Steven Waldren, Bernard Ewigman and Wayne Altman
The Journal of the American Board of Family Medicine July 2024, 37 (4) 520-524; DOI: https://doi.org/10.3122/jabfm.2023.230360R1
Karim Hanna
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
MD, FAAFP
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David Chartash
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
PhD
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Winston Liaw
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
MD
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Damian Archer
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
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Daniel Parente
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
MD, PhD
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Nipa R. Shah
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
MD
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Steven Waldren
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
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Bernard Ewigman
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
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Wayne Altman
From the University of South Florida Morsani College of Medicine, Tampa, FL (KH); Yale School of Medicine and UCD, New Haven, CT (DC); University of Houston, Houston, TX (WL); North Shore Community Health, Boston, MA (DA); University of Kansas Medical Center, Kansas City, KA (DP); University of Florida, Jacksonville, Jacksonville, FL (NRS); American Academy of Family Physicians, Kansas City, MO (SW); Department of Family Medicine, Tufts University, Chicago, IL (BE); Department of Family Medicine, Tufts University, Boston, MA (WA).
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Abstract

Artificial Intelligence (AI) is poised to revolutionize family medicine, offering a transformative approach to achieving the Quintuple Aim. This article examines the imperative for family medicine to adapt to the rapidly evolving field of AI, with an emphasis on its integration in clinical practice. AI's recent advancements have the potential to significantly transform health care. We argue for the proactive engagement of family medicine in directing AI technologies toward enhancing the “Quintuple Aim.”

The article highlights potential benefits of AI, such as improved patient outcomes through enhanced diagnostic tools, clinician well-being through reduced administrative burdens, and the promotion of health equity by analyzing diverse data sets. However, we also acknowledge the risks associated with AI, including the potential for automation to diverge from patient-centered care and exacerbate health care disparities. Our recommendations stress the need for family medicine education to incorporate AI literacy, the development of a collaborative for AI integration, and the establishment of guidelines and standards through interdisciplinary cooperation. We conclude that although AI poses challenges, its responsible and ethical implementation can revolutionize family medicine, optimizing patient care and enhancing the role of clinicians in a technology-driven future.

  • Automation
  • Artificial Intelligence
  • Family Medicine
  • Information Technology
  • Medical Informatics
  • Technology Assessment
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The Journal of the American Board of Family     Medicine: 37 (4)
The Journal of the American Board of Family Medicine
Vol. 37, Issue 4
July-August 2024
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Family Medicine Must Prepare for Artificial Intelligence
Karim Hanna, David Chartash, Winston Liaw, Damian Archer, Daniel Parente, Nipa R. Shah, Steven Waldren, Bernard Ewigman, Wayne Altman
The Journal of the American Board of Family Medicine Jul 2024, 37 (4) 520-524; DOI: 10.3122/jabfm.2023.230360R1

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Family Medicine Must Prepare for Artificial Intelligence
Karim Hanna, David Chartash, Winston Liaw, Damian Archer, Daniel Parente, Nipa R. Shah, Steven Waldren, Bernard Ewigman, Wayne Altman
The Journal of the American Board of Family Medicine Jul 2024, 37 (4) 520-524; DOI: 10.3122/jabfm.2023.230360R1
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    • Improving Patient Outcomes
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Keywords

  • Automation
  • Artificial Intelligence
  • Family Medicine
  • Information Technology
  • Medical Informatics
  • Technology Assessment

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