Evidence map›Paper›PMID 41709948›Full record

ArticleAdvances in medical education and practice2026

AI Scribe Use in Residency Training: A Call for Specialty Society Guidance in Graduate Medical Education.

Julia A Giordano, Elizabeth Jones

Abstract read
In one paragraph

Article in Advances in medical education and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Julia A GiordanoPerelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.ORCID 0009-0007-1689-0983
Elizabeth JonesDepartment of Dermatology & Cutaneous Biology, Sidney Kimmel Medical College, Philadelphia, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly used for documentation purposes in clinical practice, yet guidance for resident use is limited. Given the substantial documentation burden on medical trainees, AI-powered scribing tools may offer benefits, but their integration into residency training raises educational, supervisory, and patient safety considerations. This study aimed to assess the availability of resident-specific guidance on AI scribe use from major medical and specialty organizations and to summarize current evidence on AI scribes in residency. We reviewed five major medical and specialty society websites (AAD, AMA, ACGME, AAMC, ABMS) via website searches and direct emails and conducted a PubMed search for residency-focused studies. No organization provided resident-specific guidelines; the AMA noted the need for safe, accurate, and non-discriminatory AI use, and the AAMC issued general principles for the use of AI in medical education, none of which specifically address resident documentation. Literature review identified two studies evaluating AI-assisted documentation in the residency context. The studies suggest reduced documentation burden and cognitive load, with documentation quality comparable to resident physicians. These findings highlight the need for residency-specific AI scribe consensus guidelines that address resident review, note sign-off, accountability, patient consent, and scope of use to ensure safe and effective integration into training.

Indexed as

ambient scribingartificial intelligencedocumentation burdengraduate medical educationmedical documentationpatient safetyresidency training

Identifiers

PMID41709948
PMCPMC12912149

What OpenQuestion holds

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LicenceCC BY-NC
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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.