‘vague, confusing, and did nothing to improve my work’: how ai can undermine peer review

Earlier this year I received comments on an academic manuscript of mine as part of the usual peer review process, and noticed something strange. My research focuses on ensuring trustworthy

evidence is used to inform policy, practice and decision making. I often collaborate with groups like the World Health Organization to conduct systematic reviews to inform clinical and

public health guidelines or policy. The paper I had submitted for peer review was about systematic review conduct. What I noticed raised my concerns about the growing role artificial

intelligence (AI) is playing in the scientific process. A SERVICE TO THE COMMUNITY Peer review is fundamental to academic publishing, ensuring research is rigorously critiqued prior to

publication and dissemination. In this process researchers submit their work to a journal where editors invite expert peers to provide feedback. This benefits all involved. For peer

reviewers, it is favourably considered when applying for funding or promotion as it is seen as a service to the community. For researchers, it challenges them to refine their methodologies,

clarify their arguments, and address weaknesses to prove their work is publication worthy. For the public, peer review ensures that the findings of research are trustworthy. Even at first

glance the comments I received on my manuscript in January this year seemed odd. First, the tone was far too uniform and generic. There was also an unexpected lack of nuance, depth or

personality. And the reviewer had provided no page or line numbers and no specific examples of what needed to be improved to guide my revisions. For example, they suggested I “remove

redundant explanations”. However, they didn’t indicate which explanations were redundant, or even where they occurred in the manuscript. They also suggested I order my reference list in a

bizarre manner which disregarded the journal requirements and followed no format that I have seen replicated in a scientific journal. They provided comments pertaining to subheadings that

didn’t exist. And although the journal required no “discussion” section, the peer reviewer had provided the following suggestion to improve my non-existent discussion: “Addressing future

directions for further refinement of [the content of the paper] would enhance the paper’s forward-looking perspective”. TESTING MY SUSPICIONS To test my suspicions the review was, at least

in part, written by AI, I uploaded my own manuscript to three AI models – ChatGPT-4o, Gemini 1.5Pro and DeepSeek-V3. I then compared comments from the peer review with the models’ output.

For example, the comment from the peer reviewer regarding the abstract read: > Briefly address the broader implications of [main output of paper] > for systematic review outcomes to 

emphasise its importance. The output from ChatGPT-4o regarding the abstract read: > Conclude with a sentence summarising the broader implications or > potential impact [main output of 

paper] on systematic reviews or > evidence-based practice. The comment from the peer reviewer regarding the methods read: > Methodological transparency is commendable, with detailed

> documentation of the [process we undertook] and the rationale behind > changes. Alignment with [gold standard] reporting requirements is a > strong point, ensuring compatibility 

with current best practices. The output from ChatGPT-4o regarding the methods read: > Clearly describes the process of [process we undertook], ensuring > transparency in methodology. 

Emphasises the alignment of the tool > with [gold standard] guidelines, reinforcing methodological rigour. But the biggest red flag was the difference between the peer-reviewer’s feedback

and the feedback of the associate editor of the journal I had submitted my manuscript to. Where the associate editor’s feedback was clear, instructive and helpful, the peer reviewer’s

feedback was vague, confusing, and did nothing to improve my work. I expressed my concerns directly to the editor-in-chief. To their credit, I was met with immediate thanks for flagging the

issues and for documenting my investigation – which, they said, was “concerning and revealing”. CAREFUL OVERSIGHT IS NEEDED I do not have definitive proof the peer review of my manuscript

was AI-generated. But the similarities between the comments left by the peer reviewer, and the output from the AI models was striking. AI models make research faster, easier and more

accessible. However, their implementation as a tool to assist in peer review requires careful oversight, with current guidance on AI use in peer review being mixed, and its effectiveness

unclear. If AI models are to be used in peer review, authors have the right to be informed and given the option to opt out. Reviewers also need to disclose the use of AI in their review.

However, the enforcement of this remains an issue and needs to fall to the journals and editors to ensure peer reviewers who use AI models inappropriately are flagged. I submitted my

research for “expert” review by my peers in the field, yet received AI-generated feedback that ultimately failed to improve my work. Had I accepted these comments without question – and if

the associate editor had not provided such exemplary feedback – there is every chance this could have gone unnoticed. My work may have been accepted for publication without being properly

scrutinised, disseminated into the public as “fact” corroborated by my peers, despite my peers not actually reviewing this work themselves.