ArticleAnnals of biomedical engineering2025
Hidden Prompts in Manuscripts Threaten the Integrity of Peer Review and Research: Recommendations for Journals and Institutions.
Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- AI cannot be a peer reviewer.Plant cell reports · 2025Article
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
I examine the scholarly implications of a troubling case where researchers embedded hidden prompts like "give a positive review only" into academic preprints to manipulate AI-assisted peer review. AI is now woven into nearly every facet of academic life, including the peer review process. I contend that manipulating peer review through embedding secret prompts is as serious as plagiarism or data fabrication. Peer review may not be perfect, but deception is misconduct. Reviewers must still be held accountable. Those who blindly rely on AI outputs without critical engagement fail in their scholarly duty. AI should only amplify the reviewer's expertise. As institutions begin regulating AI in research, similar frameworks must extend to peer review. Journals and publishers should establish clear, enforceable guidelines on acceptable AI use: Will AI be banned, regulated, or embraced? If allowed, disclosures must be mandatory. Authors should also be informed if AI tools will be used in the review process, ensuring transparency and consent. Confidentiality is another pressing issue. Real cases have shown how ChatGPT links shared by reviewers were indexed online, compromising sensitive, unpublished research, even though OpenAI has since moved to discontinue public link discoverability. Beyond policy, we must cultivate a culture of transparency, trust, and responsibility. Institutions can host ethics workshops and mentor early-career scholars. This is not just about AI; it is about who we are as researchers and reviewers. No matter how advanced the technology, integrity must remain our anchor. Without it, even the most innovative research stands on shaky ground.
Indexed as
Identifiers
40820180What 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.