Evidence map›Paper›PMID 41627656›Full record

ArticleJournal of general internal medicine2026

Integrating AI Scribes into Medical Education: Guardrails for Preserving Clinical Reasoning.

Jane Abernethy, Anna Shah, Belinda Chen, Stasia Reynolds, Scott M Wright, Paul O'Rourke

Erratum issuedAbstract readEditorial
In one paragraph

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. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Jane AbernethyJohns Hopkins Bayview Medical Center, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jaberne1@jhmi.edu.
Anna ShahDivision of General Internal Medicine, University of South Florida, Tampa, FL, USA.
Belinda ChenJohns Hopkins Bayview Medical Center, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Stasia ReynoldsJohns Hopkins Bayview Medical Center, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Scott M WrightJohns Hopkins Bayview Medical Center, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Paul O'RourkeJohns Hopkins Bayview Medical Center, Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical documentation is a cornerstone of physician training, not only as a record of care but as a catalyst for clinical reasoning. For medical trainees, writing notes compels them to prioritize information, justify decisions, and synthesize fragmented data into coherent narratives. With the emergence of artificial intelligence (AI) scribes that auto-generate clinical notes from ambient audio, the task of composing notes is increasingly outsourced, raising questions about its impact on education. At our internal medicine residency program, we piloted a 6-month implementation of an AI scribe tool with 48 residents, generating nearly 1000 notes. We propose seven best practices-mapped to the Accreditation Council for Graduate Medical Education (ACGME) Core Competencies-with the goal that AI scribes support, rather than erode, the development of reflective practice and diagnostic thinking. These include establishing baseline documentation skills, structured AI training, critical review of AI-generated notes, and new opportunities for feedback. In this formative moment, educators must guide learners to use AI as a scaffold for reasoning, not a substitute for it.

Indexed as

Artificial IntelligenceClinical ReasoningDocumentationEducation, Medical, GraduateInternal MedicineInternship and ResidencyClinical CompetenceHumansAI scribeambulatory careartificial intelligenceclinical reasoningmedical education

Identifiers

PMID41627656
PMCPMC13305216

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.