Evidence map›Paper›PMID 40826224›Full record

ArticleNPJ digital medicine2025

Peer perceptions of clinicians using generative AI in medical decision-making.

Haiyang Yang, Tinglong Dai, Nestoras Mathioudakis, Amy M Knight, Yuna Nakayasu, Risa M Wolf

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
  2. Article
  3. Patterns of AI Use in Clinical Work by Hospitalists: Survey Study.Journal of medical Internet research · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. A Brief Review of Artificial Intelligence in Living Kidney Donation.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    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

6 authors.

Haiyang YangCarey Business School, Johns Hopkins University, Baltimore, MD, USA.
Tinglong DaiCarey Business School, Johns Hopkins University, Baltimore, MD, USA. dai@jhu.edu.
Nestoras MathioudakisSchool of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Amy M KnightSchool of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Yuna NakayasuCarey Business School, Johns Hopkins University, Baltimore, MD, USA.
Risa M WolfCarey Business School, Johns Hopkins University, Baltimore, MD, USA. rwolf@jhu.edu.

Funding

Johns Hopkins Discovery Award 2022-2025
6 · The paper itself

Abstract

This study investigates how a physician's use of generative AI (GenAI) in medical decision‑making is perceived by peer clinicians. In a randomized experiment, 276 practicing clinicians evaluated one of three vignettes depicting a physician: (1) using no GenAI (Control), (2) using GenAI as a primary decision-making tool (GenAI-primary), and (3) using GenAI as a verification tool (GenAI-verify). Participants rated the physician depicted in the GenAI‑primary condition significantly lower in clinical skill (on a 1-7 scale; mean = 3.79) than in the Control condition (5.93, p < 0.001). Framing GenAI use as verification partially mitigated this effect (4.99, p < 0.001). Similar patterns appeared for perceived overall healthcare experience and competence. Participants also acknowledged GenAI's value in improving accuracy (4.30, p < 0.002) and rated institutionally customized GenAI more favorably (4.96, p < 0.001). These findings suggest that while clinicians see GenAI as helpful, its use can negatively impact peer evaluations. These effects can be reduced, but not fully eliminated, by framing it as a verification aid.

Identifiers

PMID40826224
PMCPMC12361413

What Socratic holds

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Registered trials

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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 Socratic graph.