Attitudes and perceptions toward the use of artificial intelligence chatbots for peer review in medical journals: a large-scale, international cross-sectional survey.
17 affiliations
- 1Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany. [email protected].
- 2Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany. [email protected].
- 3School of Public Health, Faculty of Health, University of Technology Sydney, Sydney, Australia. [email protected].
- 4Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada. [email protected].
- 5Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany.
- 6Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany.
- 7Department of Epidemiology and Data Science, Amsterdam Universities Medical Center, Amsterdam, The Netherlands.
- 8Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- 9School of Medicine, Toronto Metropolitan University, Toronto, Ontario, Canada.
- 10Institute of Health Policy, Management & Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
- 11Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
- 12JAMA and the JAMA Network, Chicago, Illinois, USA.
- 13Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
- 14Consultant, Lyon, France.
- 15Scientific Committee, Institute of Research and Action On Fraud and Plagiarism in Academia (IRAFPA), Geneva, Switzerland.
- 16Department of Research in Biomedicine and Health and Center for Evidence-Based Medicine, University of Split School of Medicine, Split, Croatia.
- 17School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Abstract
Background
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.Methods
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.Results
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%).Conclusions
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.
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BibTeX
@article{ng2026attitudes,
title = {{Attitudes and perceptions toward the use of artificial intelligence chatbots for peer review in medical journals: a large-scale, international cross-sectional survey.}},
author = {Jeremy Y Ng and Daivat Bhavsar and Neha Dhanvanthry and Lex Bouter and Teresa Chan and Holger Cramer and Annette Flanagin and Alfonso Iorio and Cynthia Lokker and Hervé Maisonneuve and Ana Marušić and David Moher},
journal = {Research integrity and peer review},
year = {2026},
month = oct,
doi = {10.1186/s41073-026-00256-5},
url = {https://europepmc.org/article/MED/42958382},
note = {CC BY},
}
RIS
TY - JOUR
TI - Attitudes and perceptions toward the use of artificial intelligence chatbots for peer review in medical journals: a large-scale, international cross-sectional survey.
AU - Jeremy Y Ng
AU - Daivat Bhavsar
AU - Neha Dhanvanthry
AU - Lex Bouter
AU - Teresa Chan
AU - Holger Cramer
AU - Annette Flanagin
AU - Alfonso Iorio
AU - Cynthia Lokker
AU - Hervé Maisonneuve
AU - Ana Marušić
AU - David Moher
PY - 2026
DA - 2026/10/26
JO - Research integrity and peer review
DO - 10.1186/s41073-026-00256-5
UR - https://europepmc.org/article/MED/42958382
AB - Background 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.Methods 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.Results 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%).Conclusions 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.
SN - 2058-8615
ER -