Ensuring peer review integrity in the era of large language models: A critical stocktaking of challenges, red flags, and recommendations
Open accessAbstract
The rise of large language models (LLMs) like ChatGPT offers promising academic opportunities but also raises peer review concerns. Reviewers increasingly use LLMs to refine language or draft reviews, blurring the line between using artificial intelligence (AI) as a supportive tool and a leading role in the peer review of manuscripts. Given the impracticality of enforcing a ban on reviewers' use of LLMs, this obscurity poses challenges for editorial teams in maintaining peer review integrity. Based on the literature and authors' editorial experience, this brief paper examines the challenges of detecting LLM-shaped reviews, highlights key indicators, and offers recommendations to uphold the rigor of peer review—an imperfect system, yet still the best we have in scholarly publishing.
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Existing LLM reviewer systems often generate unstructured outputs that vary substantially depending on prompting strategies, limiting reproducibility, traceability and editor interpretability.
“Existing LLM reviewer systems often generate unstructured outputs that vary substantially depending on prompting strategies, limiting reproducibility, traceability and editor interpretability (Sun 2025;Kocak et al. 2025).”
From Who Reviews the Reviewer? A Multi‐Agent LLM Architecture With Meta‐Review Synthesis for Editorial Peer Review (2026) · cites this paper as Kocak et al. 2025
The Introduction says that a slight change in prompt wording can change an LLM’s response, supporting the concern about reliability. It also discusses opacity, but does not say that LLM reviewer systems often produce unstructured outputs or that their outputs limit editor interpretability. Its red-flags discussion instead describes LLM-shaped reviews as stylistically consistent and often organized with headings or bullet points. No page number is available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredDistinguishing between fully AI-generated reviews and human-authored reviews refined with AI assistance remains extremely difficult.
“Researchers note that distinguishing between fully AI-generated reviews and human-authored reviews refined with AI assistance remains extremely difficult (Kocak et al. 2025).”
From Who Reviews the Reviewer? A Multi‐Agent LLM Architecture With Meta‐Review Synthesis for Editorial Peer Review (2026) · cites this paper as Kocak et al. 2025
Existing AI-review systems often operate as opaque evaluators, making it difficult for editors to inspect why certain conclusions were generated.
“Existing AI-review systems often operate as opaque evaluators, making it difficult for editors to inspect why certain conclusions were generated (Sun 2025;Seghier 2025;Kocak et al. 2025).”
From Who Reviews the Reviewer? A Multi‐Agent LLM Architecture With Meta‐Review Synthesis for Editorial Peer Review (2026) · cites this paper as Kocak et al. 2025
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- Authors
- Kocak, B., Onur, M. R., Park, S. H., Baltzer, P., & Dietzel, M.
- Affiliations
- Hacettepe University (TR)
- Asan Medical Center (KR)
- Medical University of Vienna (AT)
- Universitätsklinikum Erlangen (DE)
- Venue
- European Journal of Radiology Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 2
- Pages
- 100018
- ISSN
- 3050-5771 (ISSN-L)
- OpenAlex
- W4409486296
- Cited by
- 18
- References
- 32
- Citations by year
- 2026: 14 · 2025: 4
- FWCI
- 67.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- European Journal of Radiology Artificial Intelligence (publishedVersion, cc-by)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- large language models, peer review integrity, ChatGPT, editorial policy, scholarly publishing, AI in peer review
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