Peer review is the cornerstone of scientific credibility. Editors, authors, and readers rely on expert evaluation to maintain research quality. Traditionally, peer review depends on trust: editors assume reviews reflect human judgment, reviewers assume confidentiality, and readers expect rigorous evaluation.
Recent evidence, reported by Nature, reveals a challenge: AI-generated peer reviews can convincingly mimic human reports. These reviews are often detailed, coherent, and professional enough to pass undetected through editorial processes. With growing reviewer fatigue and rising submission volumes, AI-written reviews can exploit systemic gaps, raising questions about trust, accountability, and oversight in modern publishing.
The trust-based architecture of peer review
Peer review is less about formal verification than social and professional trust. Editors judge reviews primarily by clarity, structure, and adherence to disciplinary conventions. Human reviewers are assumed to provide nuanced insight, critique methodology, and contextualize findings.
AI-generated reviews replicate many of these signals: organized critique, balanced tone, and conventional phrasing. For editors under time pressure, these surface cues can be sufficient to pass a review as credible.
The labor behind expert assessment
Producing a detailed, methodologically sound review requires hours of unpaid work. Reviewers must critically analyze experimental design, assess statistical methods, and place results within a broader literature. AI-generated reviews, by contrast, can produce polished summaries rapidly, creating both efficiency gains and potential vulnerabilities in the editorial system.
AI as a new participant in peer review
Generative AI, including large language models (LLMs), is increasingly integrated into scientific workflows, which includes for writing, summarizing, and drafting. Peer review, however, is fundamentally evaluative. LLMs generate text based on statistical patterns in large corpora, including scientific literature, producing human-like structure, tone, and language.
AI can summarize papers, identify strengths and weaknesses, and generate recommendations. While it may lack deep methodological insight, its professional style allows it to influence editorial decisions. The Nature report indicates that undisclosed AI-generated reviews blur responsibility and accountability, since editors and readers cannot detect the author’s identity or the review’s origin.
The problem is not that the reviews are nonsensical, but that they are competent enough to pass unnoticed.
Detection limitations and editorial challenges
Detection tools attempt to identify AI text through statistical and stylistic analyses. However, scientific writing is already formulaic: cautious phrasing, structured critique, and conventional language patterns are typical. As AI learns from scholarly texts, these distinctions blur.
In Nature’s study, many AI-generated reviews were misidentified as human. Even when lacking detailed methodological analysis, AI’s coherent tone allows these reviews to integrate seamlessly into editorial workflows. This exposes systemic vulnerabilities and raises ethical questions about transparency and integrity.
Experimental comparisons with human reviewers
Researchers selected 20 cancer biology papers and generated AI reviews for comparison with human referee reports. Reviews were assessed for tone, specificity, and professional structure. Detection tools flagged only a minority as AI-generated.
AI reviews consistently summarized findings accurately, highlighted strengths and weaknesses, and produced a recommendation. While lacking deep technical critique, their polished presentation made them nearly indistinguishable from overextended human reviewers’ submissions.
Implications for editors
Editors may unknowingly rely on AI-generated evaluations for decision-making. Acceptance, revision, and rejection often hinge on reviewer confidence. AI’s ability to generate competent-sounding reviews without true expertise introduces subtle risks: bias, shallow methodological critique, or unintended influence on citation patterns.
Ethical concerns and responsibilities
Ethical frameworks typically allow AI assistance in writing, provided disclosure is clear. Fully generating peer reviews without acknowledgment is problematic, as responsibility becomes unclear. Current policies primarily target authors, not reviewers, leaving gaps in oversight. Journals face the challenge of balancing efficiency with maintaining human judgment and accountability.
Since peer review is usually anonymous, enforcement is difficult. Editors cannot reliably verify authorship of a review. Reviewers may not perceive AI use as unethical, further complicating responsibility. Maintaining trust requires transparency, formal guidelines, and awareness of potential AI influence.
Journal responses and editorial strategies
Some journals are training editors to identify AI patterns and probe for deep, paper-specific insight. While useful, even experienced editors face inherent limits in distinguishing AI from human reviews. Detection software alone is insufficient; systemic adaptation is necessary. Policies emphasizing disclosure, training, and accountability may help preserve the human-centered peer review model.
The challenge is not stopping AI, but deciding where human judgment must remain non-negotiable.
Impact on the scientific publishing ecosystem
AI-written peer reviews have the potential to reshape the scientific publishing landscape by streamlining editorial workflows and reducing reviewer burden. On one hand, they can accelerate review timelines, provide consistent formatting and clarity, and support overextended human reviewers, improving efficiency in high-volume journals. On the other hand, reliance on AI may dilute expert judgment, introduce subtle biases, and raise ethical concerns around accountability and transparency. While responsible usage could enhance the speed and readability of peer feedback, misuse or undisclosed AI involvement could compromise research integrity, erode trust in the system, and reduce the depth of methodological scrutiny. Balancing these advantages and risks is essential for maintaining content quality and credibility in scientific publishing.
AI can make peer review faster and more consistent, but the trade-off is potentially reduced expert oversight and subtle biases that may influence publication decisions.
Advantages:
- Faster turnaround times.
- Consistent feedback formatting.
- Reduced reviewer burden, especially amid increasing submission volumes.
Potential downsides:
- Dilution of expert judgment.
- Unconscious bias introduction.
- Ethical ambiguities regarding responsibility.
- Risk of eroding trust if undisclosed.
Responsible, transparent integration could improve efficiency and clarity. Misuse, however, threatens the integrity of scientific discourse and could compromise methodological scrutiny. Balancing AI assistance with human oversight is critical to maintain content quality and credibility.
Real-world illustrations
Consider a high-volume journal receiving hundreds of submissions weekly. Human reviewers may provide uneven depth due to workload. AI-generated reviews could ensure uniformity in tone and coverage but may miss subtle experimental flaws. Conversely, AI can accelerate feedback cycles, enabling faster dissemination of findings—highlighting the tension between efficiency and depth.
Frequently asked questions about AI-written peer reviews
- What are AI-written peer reviews?
AI-written peer reviews are referee reports generated by large language models that mimic human reviewers’ tone, structure, and critique. - Can AI-written reviews influence editorial decisions?
Yes. Because they can appear professional and coherent, editors may base acceptance or rejection decisions on AI-generated content unknowingly. - Are AI-generated peer reviews detectable?
Detection tools exist, but studies show many AI-written reviews pass as human due to conventional language patterns in scientific writing. - Is it ethical to use AI in peer review?
Limited language assistance is typically acceptable if disclosed, but fully generating peer reviews without disclosure is considered ethically problematic. - How can journals adapt to AI-written reviews?
Policies emphasizing transparency, editorial training, and critical evaluation of review content are recommended to maintain integrity.
Sources
- Singh Chawla D. ‘A serious problem’: peer reviews created using AI can avoid detection. Nature. 2025 Dec 19. doi: 10.1038/d41586-025-04032-1.
- Committee on Publication Ethics (COPE). https://publicationethics.org
- International Committee of Medical Journal Editors (ICMJE). http://www.icmje.org
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