AI-generated responses in peer review pose a growing challenge for reviewers and editors: Call for a reviewer rating system

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61383082%3A_____%2F25%3A00001586" target="_blank" >RIV/61383082:_____/25:00001586 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/25:10490986
Result on the web
<a href="https://pubmed.ncbi.nlm.nih.gov/39798465/" target="_blank" >https://pubmed.ncbi.nlm.nih.gov/39798465/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jocn.2025.111042" target="_blank" >10.1016/j.jocn.2025.111042</a>

Result language
angličtina
Original language name
AI-generated responses in peer review pose a growing challenge for reviewers and editors: Call for a reviewer rating system
Original language description
As a peer reviewer for the Journal of Clinical Neuroscience, I recently encountered a situation that raised serious concerns about the use of large language models (LLMs) in the academic publishing process. The manuscript under review was an original research article addressing an important topic in neurosurgery, and after my initial review, I recommended a “major revision.” The authors were required to provide substantive revisions and detailed responses to my comments, which is standard in the peer-review process for improving the quality of scientific discourse.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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