. 2025 Feb;40(3):694-702.

doi: 10.1007/s11606-024-09102-0. Epub 2024 Nov 12.

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

Byron Crowe^{1

2}, Shreya Shah^{3

4}, Derek Teng^{5

6}, Stephen P Ma⁷, Matthew DeCamp⁸, Eric I Rosenberg⁹, Jorge A Rodriguez^{6

10}, Benjamin X Collins^{11

12}, Kathryn Huber¹³, Kyle Karches¹⁴, Shana Zucker¹⁵, Eun Ji Kim¹⁶, Lisa Rotenstein¹⁷, Adam Rodman^{5

6}, Danielle Jones¹⁸, Ilana B Richman¹⁹, Tracey L Henry¹⁸, Diane Somlo^{6

20}, Samantha I Pitts²¹, Jonathan H Chen^{22

7

23}, Rebecca G Mishuris^{6

10

24}

Affiliations

¹ Division of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. bcrowe@bidmc.harvard.edu.
² Harvard Medical School, Boston, MA, USA. bcrowe@bidmc.harvard.edu.
³ Department of Medicine, Stanford University, Palo Alto, CA, USA.
⁴ Division of Primary Care and Population Health, Stanford Healthcare AI Applied Research Team, Stanford University School of Medicine, Palo Alto, CA, USA.
⁵ Division of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
⁶ Harvard Medical School, Boston, MA, USA.
⁷ Division of Hospital Medicine, Stanford, CA, USA.
⁸ Department of Medicine, University of Colorado, Aurora, CO, USA.
⁹ Division of General Internal Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL, USA.
¹⁰ Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹¹ Division of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA.
¹² Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
¹³ Department of Internal Medicine, Kaiser Permanente, Denver, CO, School of Medicine, University of Colorado, Aurora, CO, USA.
¹⁴ Department of Internal Medicine, Saint Louis University, Saint Louis, MO, USA.
¹⁵ Department of Internal Medicine, University of Miami Miller School of Medicine, Jackson Memorial Hospital, Miami, FL, USA.
¹⁶ Northwell Health, New Hyde Park, NY, USA.
¹⁷ Divisions of General Internal Medicine and Clinical Informatics, Department of Medicine, University of California at San Francisco, San Francisco, CA, USA.
¹⁸ Division of General Internal Medicine, Emory University School of Medicine, Atlanta, GA, USA.
¹⁹ Section of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
²⁰ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
²¹ Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²² Stanford Center for Biomedical Informatics Research, Stanford, CA, USA.
²³ Clinical Excellence Research Center, Stanford, CA, USA.
²⁴ Digital, Mass General Brigham, Somerville, MA, USA.

PMID: 39531100
PMCID: PMC11861482
DOI: 10.1007/s11606-024-09102-0

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

Byron Crowe et al. J Gen Intern Med. 2025 Feb.

. 2025 Feb;40(3):694-702.

doi: 10.1007/s11606-024-09102-0. Epub 2024 Nov 12.

Authors

Affiliations

¹ Division of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. bcrowe@bidmc.harvard.edu.
² Harvard Medical School, Boston, MA, USA. bcrowe@bidmc.harvard.edu.
³ Department of Medicine, Stanford University, Palo Alto, CA, USA.
⁴ Division of Primary Care and Population Health, Stanford Healthcare AI Applied Research Team, Stanford University School of Medicine, Palo Alto, CA, USA.
⁵ Division of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
⁶ Harvard Medical School, Boston, MA, USA.
⁷ Division of Hospital Medicine, Stanford, CA, USA.
⁸ Department of Medicine, University of Colorado, Aurora, CO, USA.
⁹ Division of General Internal Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL, USA.
¹⁰ Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹¹ Division of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA.
¹² Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
¹³ Department of Internal Medicine, Kaiser Permanente, Denver, CO, School of Medicine, University of Colorado, Aurora, CO, USA.
¹⁴ Department of Internal Medicine, Saint Louis University, Saint Louis, MO, USA.
¹⁵ Department of Internal Medicine, University of Miami Miller School of Medicine, Jackson Memorial Hospital, Miami, FL, USA.
¹⁶ Northwell Health, New Hyde Park, NY, USA.
¹⁷ Divisions of General Internal Medicine and Clinical Informatics, Department of Medicine, University of California at San Francisco, San Francisco, CA, USA.
¹⁸ Division of General Internal Medicine, Emory University School of Medicine, Atlanta, GA, USA.
¹⁹ Section of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
²⁰ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
²¹ Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²² Stanford Center for Biomedical Informatics Research, Stanford, CA, USA.
²³ Clinical Excellence Research Center, Stanford, CA, USA.
²⁴ Digital, Mass General Brigham, Somerville, MA, USA.

PMID: 39531100
PMCID: PMC11861482
DOI: 10.1007/s11606-024-09102-0

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.

Keywords: artificial intelligence; clinical practice; healthcare technology.

PubMed Disclaimer

Conflict of interest statement

Declarations. Conflict of Interest: BC reports employment and equity with Solera Health outside the submitted work. MD reports consulting on ethics policy issues for the American College of Physicians via an institutional contract. JAR reports serving as a consultant for the Association of American Medical Colleges. EK reports funding from the NIH through K23HL163498 unrelated to the current work. LR reports research funding from FeelBetter Inc, the Agency for Healthcare Research and Quality, the Physicians Foundation, and the American Medical Association. She also serves on the AI Advisory Council for Augmedix, Inc and has received honoraria from Phreesia, Inc. AR reports funding from the Gordon and Betty Moore foundation for research on large language models. JC reports research funding support in part by NIH/National Institute of Allergy and Infectious Diseases (1R01AI17812101), NIH/National Institute on Drug Abuse Clinical Trials Network (UG1DA015815 - CTN-0136), Gordon and Betty Moore Foundation (Grant #12409), Stanford Artificial Intelligence in Medicine and Imaging - Human-Centered Artificial Intelligence (AIMI-HAI) Partnership Grant, American Heart Association - Strategically Focused Research Network - Diversity in Clinical Trials. Additionally, JC reports being co-founder of Reaction Explorer LLC that develops and licenses organic chemistry education software, paid consulting fees from Sutton Pierce, Younker Hyde MacFarlane, and Sykes McAllister as a medical expert witness and paid consulting fees from ISHI Health. RGM reports advisory committee role with Elsevier, outside of this work. All other authors have no conflicts to report.

Cited by

Evaluation of the accuracy and safety of machine translation of patient-specific discharge instructions: a comparative analysis.
Kong M, Fernandez A, Bains J, Milisavljevic A, Brooks KC, Shanmugam A, Avilez L, Li J, Honcharov V, Yang A, Khoong EC. Kong M, et al. BMJ Qual Saf. 2025 Jul 9:bmjqs-2024-018384. doi: 10.1136/bmjqs-2024-018384. Online ahead of print. BMJ Qual Saf. 2025. PMID: 40633961 Free PMC article.

References

1. Shah NH, Entwistle D, Pfeffer MA. Creation and adoption of large language models in medicine. JAMA 2023;330(9):866. - PubMed
1. Minaee S, Mikolov T, Nikzad N, et al. Large Language Models: A Survey. 2024 [cited 2024 Feb 29];Available from: https://arxiv.org/abs/2402.06196
1. Bubeck S, Chandrasekaran V, Eldan R, et al. Sparks of Artificial General Intelligence: Early experiments with GPT-4. 2023 [cited 2024 Feb 29];Available from: https://arxiv.org/abs/2303.12712
1. Rodman A, Buckley TA, Manrai AK, Morgan DJ. Artificial intelligence vs clinician performance in estimating probabilities of diagnoses before and after testing. JAMA Netw Open 2023;6(12):e2347075. - PMC - PubMed
1. Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med 2023;29(8):1930–40. - PubMed

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
- PubMed Central
- Springer

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Similar articles

Cited by

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Abstract

Conflict of interest statement

Similar articles

Cited by

References

MeSH terms

Related information

Grants and funding

LinkOut - more resources

Full Text Sources