ArticleResearch integrity and peer review2025
Personal experience with AI-generated peer reviews: a case study.
Article in Research integrity and peer review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Veterinary Peer Review: Advancing Rigor or Raising New Concerns?Veterinary medicine and science · 2026Article
- Disclosure is not documentation: an open science framework for documenting generative AI use in scholarly research and publication workflows.Research integrity and peer review · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile some recent studies have looked at large language model (LLM) use in peer review at the corpus level, to date there have been few examinations of instances of AI-generated reviews in their social context. The goal of this first-person account is to present my experience of receiving two anonymous peer review reports that I believe were produced using generative AI, as well as lessons learned from that experience.
methodsThis is a case report on the timeline of the incident, and my and the journal's actions following it. Supporting evidence includes text patterns in the reports, online AI detection tools and ChatGPT simulations; recommendations are offered for others who may find themselves in a similar situation. The primary research limitation of this article is that it is based on one individual's personal experience.
resultsAfter alleging the use of generative AI in December 2023, two months of back-and-forth ensued between myself and the journal, leading to my withdrawal of the submission. The journal denied any ethical breach, without taking an explicit position on the allegations of LLM use. Based on this experience, I recommend that authors engage in dialogue with journals on AI use in peer review prior to article submission; where undisclosed AI use is suspected, authors should proactively amass evidence, request an investigation protocol, escalate the matter as needed, involve independent bodies where possible, and share their experience with fellow researchers.
conclusionsJournals need to promptly adopt transparent policies on LLM use in peer review, in particular requiring disclosure. Open peer review where identities of all stakeholders are declared might safeguard against LLM misuse, but accountability in the AI era is needed from all parties.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.