Evidence map›Paper›PMID 41888492›Full record

ArticleJournal of general internal medicine2026

Artificial Intelligence in the Internal Medicine Clerkship: Results of a National Survey.

Navin L Kumar, Casey N McQuade, Eliana Bonifacino, Irene Alexandraki, Kathryn K Hufmeyer, Michael Kisielewski, Cindy J Lai, Elexis McBee, Prashant Patel, Nora Y Osman

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Article in Journal of general internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

10 authors.

Navin L KumarBrigham and Women's Hospital, Department of Medicine, Boston, MA, USA. nlkumar@bwh.harvard.edu.ORCID http://orcid.org/0000-0002-6235-8433
Casey N McQuadeDepartment of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Eliana BonifacinoDepartment of Medicine, MedStar Washington Hospital Center, Washington D.C., USA.
Irene AlexandrakiUniversity of Oklahoma College of Medicine, Oklahoma City, OK, USA.
Kathryn K HufmeyerDepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Michael KisielewskiAlliance for Academic Internal Medicine, Alexandria, VA, USA.
Cindy J LaiDepartment of Medicine, University of California San Francisco School of Medicine, San Francisco, CA, USA.
Elexis McBeeUniformed Services University of the Health Sciences, Bethesda, USA.
Prashant PatelDepartment of Medicine, University of California San Francisco School of Medicine, San Francisco, CA, USA.
Nora Y OsmanBrigham and Women's Hospital, Department of Medicine, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeArtificial intelligence (AI) in medical education is rapidly evolving. The nascent literature about the use of AI in clerkships is sparse. In this national study, we explore how internal medicine (IM) clerkship directors (CDs) conceptualize the roles that AI may play in clerkship learning environments.

methodFrom September to December 2024, the Clerkship Directors in Internal Medicine of the Alliance for Academic Internal Medicine conducted its annual, nationally representative survey of IM CDs at 141 US and US territory-based medical schools with Liaison Committee on Medical Education accreditation. This study is based on 21 questions about the use of AI in IM clerkships.

resultsThe survey response rate was 80.1% (113/141 unique medical schools). Most CDs (58.8%) perceived that it was "somewhat important" or "very important" to incorporate the use of AI in the medical school curricula and identified several AI topics as particularly relevant to teach students in the IM clerkship. However, no respondents reported the inclusion of AI content or AI-specific teaching in their clerkship curriculum. CDs reported multiple perceived barriers to explicitly teaching about AI in the IM clerkship, with faculty and CD knowledge about AI (84.2% and 75.4%, respectively) as the leading barriers.

conclusionsThe results of this survey found that IM CDs believe that AI plays an important role in medical education and the IM clerkship, yet there is limited structured teaching about AI within their clerkships. To prepare for this new era in medical education, institutions need to invest in CD and faculty education on AI so they can safely lead students in today's learning environment.

Indexed as

artificial intelligenceclerkshipinternal medicine

Identifiers

PMID41888492

What OpenQuestion holds

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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.