Artificial intelligence in scholarly peer review: a scoping review of applications, risks, and governance challenges

Artificial intelligence (AI) is increasingly integrated into scholarly publishing workflows, extending beyond manuscript preparation into editorial triage, reviewer assistance, and policy development. Peer review simultaneously faces long-standing problems including reviewer fatigue, bias, opacity, and publish-or-perish incentives. How AI interacts with these structural weaknesses remains unclear.

To map how AI is currently used in scholarly peer review, synthesize reported benefits and risks, and identify governance and research gaps relevant to health sciences.

A scoping review following Arksey and O'Malley was conducted and reported according to PRISMA-ScR. Scopus, Web of Science, PubMed/MEDLINE, and IEEE Xplore were searched (January 1, 2024-August 31, 2025) using terms combining artificial intelligence and peer review. Grey literature (publisher policies, professional guidelines, editorials) was identified through targeted searches of COPE, ICMJE, WAME, major publisher portals, and preprint servers. Duplicate screening/extraction with adjudication were done. Data were synthesized using inductive thematic analysis.

Of 2,908 records, 189 met inclusion criteria. AI is used as AI assistive (triage, assistance) and autonomous (review generation, prediction).Reported benefits include improved workflow efficiency, standardized checks, and clearer feedback. However, current systems lack domain reasoning and ethical judgment for autonomous evaluation. Key risks are confidentiality breaches when manuscripts are submitted to third-party tools, algorithmic bias favoring elite institutions or male authors, and homogenization of scholarly voice. As of August 31, 2025, governance policies across publishers, journals, and professional societies remain fragmented. In many documented cases, reviewer use of generative AI is more restricted than author-side use; however, policies vary by publisher, journal, and society, and continue to evolve.

AI can strengthen peer review when deployed as a transparent, auditable, privacy-preserving support tool under human oversight. Responsible integration in medical informatics requires coordinated governance, bias monitoring, secure infrastructures, and reforms to evaluation incentives.