AI-assisted peer review: efficiency for reviewers, burden for authors—a narrative review

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Artificial intelligence (AI) in scholarly peer review has the potential to simplify the work of editors and reviewers through faster triage, automated reporting completeness checks, and scalable synthesis of reviewer comments. However, it also presents practical, ethical, and technical limitations that may place implementation burdens on authors. I examine bias arising from training data and model architecture, confidentiality risks from commercial AI deployment, accountability gaps when human judgment is displaced, and the growing threat of hallucinated citations to scientific integrity. I further analyze how poorly governed AI integration shifts remediation and formatting burdens onto authors—particularly those from under-resourced or non-Western institutions. I propose hybrid human–AI workflows, mandatory disclosure standards, and equity-conscious implementation frameworks to address these harms. Responsible AI integration in peer review requires transparency, human accountability, and deliberate protection of the diversity of voices represented in scientific literature. These considerations also extend beyond individual actors to recursive feedback loops in which AI-assisted manuscripts are evaluated by AI-assisted review systems, reinforcing the need for human contextual stewardship.