Attitudes and perceptions toward the use of artificial intelligence chatbots for peer review in medical journals: a large-scale, international cross-sectional survey.

Authors

Jeremy Y Ng, Daivat Bhavsar, Neha Dhanvanthry, Lex Bouter, Teresa Chan, Holger Cramer, Annette Flanagin, Alfonso Iorio, Cynthia Lokker, Hervé Maisonneuve, Ana Marušić, David Moher

Published in

Research integrity and peer review. Volume 11. Issue 1. Oct 26, 2026. Epub Oct 26, 2026.

Abstract

Artificial intelligence chatbots (AICs) are increasingly being considered for use in scholarly peer review to assist with tasks such as identifying methodological issues, verifying references, and improving language clarity. However, concerns remain regarding their reliability, ethical implications, and transparency. Evidence on how medical journal peer reviewers perceive the role and impact of AICs is limited. This study explored reviewers' familiarity with AICs, perceived benefits and challenges, ethical concerns, and anticipated future roles in peer review.
We conducted a cross-sectional online survey of medical journal peer reviewers. Corresponding author information was extracted from MEDLINE-indexed articles added to PubMed within a six-week period. A total of 72,851 authors were invited via email to participate; those who self-identified as peer reviewers were eligible. The 29-item survey assessed familiarity with AICs and perceptions of their benefits and limitations in peer review. The survey was administered via SurveyMonkey from April 28 to June 16, 2025, with two reminder emails sent during the data collection period.
Of 66,679 email invitations, a total of 1260 respondents provided survey responses (response rate: 1.9%), of whom 1157 met the eligibility criteria, and 1077 answered the main survey questions. Most participants were familiar with AICs (86.2%) and had used them for general purposes (87.7%), though most report not using AICs for peer review purposes (70.3%). Most respondents reported that their institutions do not provide training on AIC use in peer review (69.5%), although many expressed interest in such training (60.7%). Perceptions of AIC benefits were mixed, while concerns were widely shared, particularly regarding potential algorithmic bias (80.3%) and issues related to trust and user acceptance (73.3%).
While familiarity with AICs is high among medical journal peer reviewers, their reported use in peer review remains limited. There is clear interest in training and guidance, however, concerns related to ethics, data privacy, and research integrity persist and should be addressed before broader implementation.

PMID:
42958382
Bibliographic data and abstract were imported from PubMed on 30 Oct 2026.

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