Examples
Worked examples
- Is an instance
A reviewer using a spelling/grammar assistant on their own review text before submitting it, without entering any manuscript content into the tool
- Is an instance
A publisher-approved, contractually-bound integrity-screening tool run on a manuscript by the publisher itself (not the individual reviewer) as part of pre-review triage
Counter-examples
Looks similar, but isn't
- Not an instance
Pasting sections of an unpublished manuscript into a general-purpose public chatbot to "help write the review" is the specific practice nearly every major publisher now bans
- Not an instance
An editor's use of AI in reaching a decision is a distinct, separately-governed practice -- see ai-in-editorial-decisions
Editorial commentary
Confidentiality is the crux of this entry. A manuscript under review is unpublished, often not yet public in any form, and reviewers agree — implicitly or explicitly — to keep it confidential as a condition of being asked to review. Uploading that manuscript, or any substantive part of it, to a third-party generative AI tool typically means the text leaves the reviewer’s and publisher’s control and may be retained, logged, or used to train the vendor’s models, depending on the tool and its terms of service. That is a confidentiality breach independent of whether the AI-generated review itself is any good.
What publishers actually say
Elsevier’s reviewer AI policy prohibits uploading a manuscript, or any part of one, to a generative AI tool, citing confidentiality and data-privacy grounds, while permitting limited, disclosed assistive use such as wording polish that does not involve sharing manuscript content. SAGE bars reviewers from using ChatGPT-style tools to generate reviews outright, with a stated consequence of not being invited to review again. JAMA Network’s guidance states peer reviewers may not enter manuscript content or their draft reviews into chatbots, framing this explicitly as a confidentiality breach, while allowing disclosed AI use that does not involve sharing manuscript text. See AI in peer review: publisher policy comparison for a fuller side-by-side.
How this differs from related AI-band terms
- vs. AI in editorial decisions: a different role (reviewer, not editor) and a distinct point of failure — the reviewer-confidentiality breach happens the moment manuscript text is entered into a third-party tool, before any decision is made.
- vs. detection tool (AI-generated): using a detector to screen a submission for AI-generated content is a separate, publisher-run administrative function, not a reviewer using AI to evaluate the manuscript’s substance.
What is generally still permitted
Policies converge on allowing disclosed, non-content-sharing assistance: language polishing of a reviewer’s own draft review text, or general background research that does not involve pasting in manuscript content. The distinguishing question every policy asks is the same: did any part of the confidential manuscript leave the reviewer’s control into a third-party system.
Also known as
AI-assisted peer review · LLM peer review
Machine-readable encodings
Use in your systems
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