Evidence map›Paper›PMID 39531100›Full record

ArticleJournal of general internal medicine2025

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, Shreya Shah, Derek Teng, Stephen P Ma, Matthew DeCamp, Eric I Rosenberg, Jorge A Rodriguez, Benjamin X Collins, Kathryn Huber, Kyle Karches and 11 more

Abstract readConsensus Statement
In one paragraph

Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Measuring AI Productivity in Episodes of Care.Journal of general internal medicine · 2026
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Byron CroweDivision of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. bcrowe@bidmc.harvard.edu.ORCID 0000-0001-9217-1533
Shreya ShahDepartment of Medicine, Stanford University, Palo Alto, CA, USA.
Derek TengDivision of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Stephen P MaDivision of Hospital Medicine, Stanford, CA, USA.
Matthew DeCampDepartment of Medicine, University of Colorado, Aurora, CO, USA.
Eric I RosenbergDivision of General Internal Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL, USA.
Jorge A RodriguezHarvard Medical School, Boston, MA, USA.
Benjamin X CollinsDivision of General Internal Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA.
Kathryn HuberDepartment of Internal Medicine, Kaiser Permanente, Denver, CO, School of Medicine, University of Colorado, Aurora, CO, USA.
Kyle KarchesDepartment of Internal Medicine, Saint Louis University, Saint Louis, MO, USA.
Shana ZuckerDepartment of Internal Medicine, University of Miami Miller School of Medicine, Jackson Memorial Hospital, Miami, FL, USA.
Eun Ji KimNorthwell Health, New Hyde Park, NY, USA.
Lisa RotensteinDivisions of General Internal Medicine and Clinical Informatics, Department of Medicine, University of California at San Francisco, San Francisco, CA, USA.
Adam RodmanDivision of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Danielle JonesDivision of General Internal Medicine, Emory University School of Medicine, Atlanta, GA, USA.
Ilana B RichmanSection of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Tracey L HenryDivision of General Internal Medicine, Emory University School of Medicine, Atlanta, GA, USA.
Diane SomloHarvard Medical School, Boston, MA, USA.
Samantha I PittsDivision of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jonathan H ChenStanford Center for Biomedical Informatics Research, Stanford, CA, USA.
Rebecca G MishurisHarvard Medical School, Boston, MA, USA.

Funding

Toward Technology Equity: Extending Telemedicine to Latino Patients with Type II DiabetesK23MD016439 · NIMHD · BRIGHAM AND WOMEN'S HOSPITAL · PI RODRIGUEZ, JORGE A · 2021 to 2025
$985k
NIMHD NIH HHS K23 MD016439NIMHD NIH HHS L32 MD018892
6 · The paper itself

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.

Indexed as

Artificial IntelligenceInternal MedicineClinical Decision-MakingDelivery of Health CareGenerative Artificial IntelligenceHumansPhysician-Patient RelationsSocieties, Medicalartificial intelligenceclinical practicehealthcare technology

Identifiers

PMID39531100
PMCPMC11861482

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.