Chi-Ping Day, a researcher at the National Cancer Institute, NIH, reviews a lot of manuscripts. When he found tools to support his review process, he brought them to his editors. What followed is a story about where peer review is headed.
Starting the Conversation
Dr. Day has spent years reviewing manuscripts. Like many experienced researchers, he's seen the volume of papers outpace the pool of qualified reviewers.
The explosive increase in the number of new journals and the number of published papers has dramatically changed the landscape of manuscript reviewing. A researcher receives overwhelmingly more requests for review, and editors desperately search for reviewers, making the sense of academic recognition wane and eventually vanish for reviewers. Even a very motivated reviewer with a good spirit of community service can become fatigued quickly.
Rather than quietly using AI tools or waiting for journals to issue formal policies, Dr. Day took a different approach: he asked editors directly whether he could use AI tools to assist his reviews.
Many researchers actively use AI for writing and reviewing manuscripts, yet admitting it is still taboo. Why not propose the guidelines ourselves and use these tools openly?
The response has been overwhelmingly positive. He's since received permission to use professional AI review tools, including Reviewer3. Editors have been receptive, recognizing that AI-assisted review can help address the growing backlog while raising the bar for quality.
How He Uses AI in His Review Workflow
When Dr. Day reviews a manuscript, he runs it through professional AI review tools like Reviewer3 alongside his own reading. It performs a technical audit of a research paper, identifying methodological gaps, verifying references and statistics, and surfacing logical inconsistencies. Each comment is linked directly to the paper, so he can quickly see whether the data support the major conclusions of the work.
I use it to check whether I missed any technical issues. It saves me a lot of time determining the quality of a study.
As we've shown in our benchmark, Reviewer3 emphasizes technical verification upstream of human judgment in peer review, while humans focus on contribution. Chi-Ping uses this function as a complement to his years of expertise.
I prefer that AI review tools do not evaluate novelty or significance. It is the researcher's responsibility, based on their expertise and knowledge, to justify those for a study. Tools like Reviewer3 handle the technical verification — whether the data support the claims — so I can focus on the judgment only a human reviewer can bring.
Transparency as a Model
By getting explicit permission from editors, Dr. Day is establishing a transparent model for adopting these tools responsibly. He draws an analogy from Isaac Asimov, the mid-century science-fiction writer who imagined intelligent machines long before and proposed a set of guidelines for their safe development, the famous "three laws of robotics", noting that "only when people understand how a tool will be used can it be trusted, disseminated, and improved."
For Dr. Day, the same logic applies to AI in peer review. He is particularly concerned about reviewers quietly turning to general-purpose models without any shared guidelines:
In most cases, reviewers simply use general-purpose AI like ChatGPT or Gemini with personally written prompts to review a manuscript. This brings a lot of risks of hallucination, bias, and conceptual errors.
The alternative, in his view, is for researchers to take the lead and adopt professionally trained review tools in the open.
Reviewers do not have to passively take on tasks with the full burden on their shoulders. We can call for the right tools to improve the peer review process.
Dr. Day's Proposed Guidelines
To help other reviewers follow his approach, Dr. Day has shared the guidelines he uses in AI-assisted peer review.
- Request permission from the editor. Respond to the review request letting the editor know you intend to use professional AI review tools under the conditions below.
- Only use tools with appropriate data privacy protection. Manuscripts under review are confidential. Before uploading a manuscript, confirm the tool does not train on your inputs, does not retain the content beyond the review session, and is explicit about where the data is stored and who can access it.
- Limit use to quality assurance. AI tools should be limited to assisting with copy editing (sentence organization, grammar), consistency of statements, statistical power in study design, selection of methods, and related checks.
- Independently verify every AI-generated claim. Citing statements from an AI-generated review report is fine, provided that you personally approve or disapprove each claim or suggestion before including it.
- Attach the AI-generated output to your final review report so editors can see exactly what the tool produced.
Looking Ahead
AI is already being used in peer review, often quietly and without disclosure. Dr. Day's worry is that if this continues, the damage to trust will be hard to undo.
If AI in peer review remains taboo or is treated as black magic, the system will distort in silence until it collapses, as trust erodes among researchers, institutions, funding agencies, and the public. There is still time to act on transparency. Guidelines must be built, and AI use must be disclosed.
Dr. Day's approach offers a concrete protocol for transparency around AI use: ask permission, use professional-grade tools, and make the process visible. The question is no longer whether AI will play a role in peer review, but how the community chooses to integrate it responsibly.
Disclosure
Dr. Chi-Ping Day is an employee of National Institutes of Health (NIH). He used the AI review tools only for his own research on ethics in AI. He declares no conflict of interest with the developers of these tools and does not endorse any commercial product mentioned in this article. His points of view do not necessarily reflect the views of the NIH or the US Department of Health and Human Services.
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