Medical reviewers know AI—but 70% in a global survey had not used it for peer review
An international survey found high familiarity with chatbots alongside limited peer-review use, scarce training and widespread concern about bias. Its 1.9% response rate sharply limits generalisation.
The short version
- What happened
- Of 1,077 eligible respondents answering the main survey, 70.3% said they had never used an AI chatbot for peer review, despite 86.2% being familiar with the tools.
- Who it affects
- Authors, patients and readers rely on confidential, technically competent review; unclear AI use can expose unpublished material or add fluent but incorrect criticism without anyone recognising the source.
- What remains unproven
- 1.9% response rate among delivered invitations
Keep in mind
1.9% response rate among delivered invitations
Self-reported attitudes and behaviour
English-only survey
Corresponding authors were screened for reviewer eligibility after responding
Cross-sectional design cannot measure change or causal effects
No direct test of peer-review quality
Source analysis · 1 linked source. Primary analysed source date: 26 October 2026.
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Familiarity has moved faster than peer-review practice
A peer-reviewed international survey found a practical gap between knowing about AI chatbots and using them in medical-journal peer review. Among 1,077 eligible respondents who answered the main questions, 86.2% were familiar with chatbots and 87.7% had used ChatGPT generally, yet 70.3% said they had never used an AI chatbot for peer review.
The more immediate issue for reviewers and editors is institutional support. Of 1,067 respondents, 69.5% reported no institutional training; 60.7% were interested in training. More than half said their institution had no policy for chatbot use in review, while concern about algorithmic bias reached 80.3%. The survey measured perceptions and self-reported behaviour—not review quality, accuracy or misconduct.[1]
A large respondent group came from a very low response rate
The team drew corresponding-author contacts from MEDLINE records, sent 72,851 invitations and recorded 6,172 bounces. It received 1,260 responses from 66,679 delivered emails: 1.9%, or 3.7% among emails recorded as opened. Respondents worked across all six WHO regions, but the Americas and Europe supplied 73.6% of the main sample and the English-only questionnaire excluded many researchers.
For journals, the finding supports clear confidentiality, disclosure and verification rules before broader adoption; it does not show that AI improves peer review. Confidence would rise with representative recruitment, observed review behaviour and blinded comparisons of human-only and AI-assisted reports for error detection, bias, time, confidentiality breaches and author benefit. The study was preregistered, unfunded and reported no competing interests.[1]
The distinction that matters
What the survey can—and cannot—establish
The broad invitation list produced useful descriptive evidence, but not a population estimate of all medical reviewers.
Invited
66,679 delivered emails
1,260 responses; 1.9% response rate
Main sample
1,077 answered core questions
Report familiarity, use, training and concerns
Not tested
No review-performance comparison
Accuracy and quality effects remain unknown
Watch the background, then check the evidence
Illustrative photo · not a video frame
Google DeepMind · 1 min 52 sec · Background explainer
Protein folding explained
YouTube loads only when you choose; playback starts when you press play.
A short introduction to why a protein’s shape matters. It provides background for understanding what AI structure prediction contributes to biological research.
Evidence check: This is the developer’s explanation. Predicting a structure does not by itself establish a treatment’s safety or benefit for patients.
Published 2020-11-30. Source checked 14 October 2026. Check captions on the publisher’s video page.
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What this means for people
- Authors, patients and readers rely on confidential, technically competent review; unclear AI use can expose unpublished material or add fluent but incorrect criticism without anyone recognising the source.
Global context
Respondents covered all six WHO regions, but the Americas and Europe made up nearly three quarters of the main sample. Policies and access to approved tools differ across publishers, institutions and languages.
What the evidence does not yet show
- 1.9% response rate among delivered invitations
- Self-reported attitudes and behaviour
- English-only survey
- Corresponding authors were screened for reviewer eligibility after responding
- Cross-sectional design cannot measure change or causal effects
- No direct test of peer-review quality
What to watch next
- Blinded comparisons of review accuracy and usefulness
- Observed rather than self-reported chatbot use
- Clear publisher rules for disclosure and manuscript confidentiality
- Representative multilingual samples
- Training evaluations that measure safer reviewer behaviour
Living evidence record
Impact record IAI-1R7Z58P
Evidence stage
Studied
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Present
Record status
Monitoring
Last checked
26 October 2026
Source trail
1 direct source across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Single-source reporting disclosure
This record analyses one direct source. It can establish what Research Integrity and Peer Review published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.
Evidence trail
Sources used for this report
Links checked 26 October 2026
Research question, topics and regions
How medical-journal peer reviewers perceive and use AI chatbots, and whether institutions provide training or policies
Topics: Peer review · Research integrity · Training · AI governance
Regions: Global
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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