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Personal experience with AI-generated peer reviews: a case study.

Nicholas Lo Vecchio1

  • 1Independent researcher, Marseille, France. nlovecchio@gmail.com.

Research Integrity and Peer Review
|April 6, 2025
PubMed
Summary

This case report details an author's experience with suspected AI-generated peer reviews, highlighting the need for journal transparency and author preparedness regarding large language model (LLM) use in scientific publishing.

Keywords:
Academic misconductChatGPTGenerative AILLMsLarge language modelsPeer review

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Area of Science:

  • Scientific Publishing
  • Artificial Intelligence in Academia
  • Scholarly Communication

Background:

  • Limited research exists on the social context of AI-generated peer reviews, despite corpus-level studies on large language model (LLM) use.
  • This article presents a first-person account of receiving peer review reports suspected to be AI-generated.

Purpose of the Study:

  • To document a personal experience with suspected AI-generated peer reviews.
  • To provide lessons learned and recommendations for authors facing similar situations.
  • To advocate for transparent policies on AI use in peer review.

Main Methods:

  • Case report detailing the timeline of an incident involving suspected AI-generated peer reviews.
  • Analysis of text patterns, AI detection tools, and simulations to support allegations.
  • Recommendations derived from personal experience and journal interactions.

Main Results:

  • A two-month dispute with the journal following allegations of AI use led to manuscript withdrawal.
  • The journal denied ethical breaches without confirming or denying LLM use.
  • The author recommends proactive author engagement with journals on AI policies and evidence gathering.

Conclusions:

  • Journals must implement transparent policies requiring disclosure of large language model (LLM) use in peer review.
  • Open peer review may mitigate AI misuse, but accountability is crucial for all parties in the AI era.