Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine.

Journal: Journal of general internal medicine
PMID:

Abstract

Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship. Additionally, we highlight our most important generative AI ethics and equity considerations for these stakeholders. For clinicians, we recommend approaching generative AI similarly to other important biomedical advancements, critically appraising its evidence and utility and incorporating it thoughtfully into practice. For technologists developing generative AI for healthcare applications, we recommend a major frameshift in thinking away from the expectation that clinicians will "supervise" generative AI. Rather, these organizations and individuals should hold themselves and their technologies to the same set of high standards expected of the clinical workforce and strive to design high-performing, well-studied tools that improve care and foster the therapeutic relationship, not simply those that improve efficiency or market share. We further recommend deep and ongoing partnerships with clinicians and patients as necessary collaborators in this work. And for healthcare organizations, we recommend pursuing a combination of both incremental and transformative change with generative AI, directing resources toward both endeavors, and avoiding the urge to rapidly displace the human clinical workforce with generative AI. We affirm that the practice of medicine remains a fundamentally human endeavor which should be enhanced by technology, not displaced by it.

Authors

  • Byron Crowe
    Division of General Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
  • Shreya Shah
    Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA.
  • Derek Teng
    Division of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Stephen P Ma
    Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, United States.
  • Matthew DeCamp
    Center for Bioethics and Humanities and Division of General Internal Medicine, University of Colorado, Aurora, CO 80045, USA. Electronic address: matthew.decamp@ucdenver.edu.
  • Eric I Rosenberg
    Division of General Internal Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL, USA.
  • Jorge A Rodriguez
    Brigham and Women's Hospital, Boston, USA.
  • Benjamin X Collins
    Department of Biomedical Informatics, Vanderbilt University Medical Center (VUMC), Nashville, TN 37203, United States.
  • Kathryn Huber
    University of Colorado School of Medicine.
  • Kyle Karches
    Department of Internal Medicine, Saint Louis University, Saint Louis, MO, USA.
  • Shana Zucker
    Department of Internal Medicine, University of Miami Miller School of Medicine, Jackson Memorial Hospital, Miami, FL, USA.
  • Eun Ji Kim
    Northwell Health, New Hyde Park, NY, USA.
  • Lisa Rotenstein
    Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.
  • Adam Rodman
    Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Danielle Jones
    Division of General Internal Medicine, Emory University School of Medicine, Atlanta, GA, USA.
  • Ilana B Richman
    Section of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Tracey L Henry
    Division of General Medicine, Emory University School of Medicine, Atlanta, GA, USA.
  • Diane Somlo
    Harvard Medical School, Boston, MA, USA.
  • Samantha I Pitts
    Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Jonathan H Chen
    Stanford Center for Biomedical Informatics Research, Stanford, CA.
  • Rebecca G Mishuris
    Harvard Medical School, Boston, MA, USA.