ARTIFICIAL INTELLIGENCE-ASSISTED PEER REVIEW: A NARRATIVE REVIEW OF CURRENT EVIDENCE, APPLICATIONS, CHALLENGES, AND FUTURE PERSPECTIVES
DOI:
https://doi.org/10.65605/a-jmrhs.2026.v04.i03.pp13-23Keywords:
Artificial Intelligence, Peer Review, Scholarly Publishing, Research Integrity, Large Language Models, Editorial Decision-Making, Publication Ethics, Human–Ai Collaboration, Bias and Transparency.Abstract
Background: Peer review remains the cornerstone of scholarly publishing, yet it faces persistent challenges including reviewer fatigue, delays, variability in quality, and increasing submission volumes. Recent advances in artificial intelligence (AI), particularly large language models and automated analytic tools, have prompted growing interest in their potential role in supporting and augmenting the peer review process.
Objective: This narrative review critically examines the current evidence on the effectiveness of AI-assisted peer review, highlighting its benefits, risks, limitations, and future prospects.
Methods: A narrative synthesis of the available literature on AI applications in peer review was conducted, focusing on empirical studies, survey data, and publisher reports published between 2019 and 2026.
Results: Available evidence suggests that AI tools can enhance efficiency by assisting with initial manuscript screening, technical checks, plagiarism detection, methodological appraisal, and structured summarization. AI-assisted peer review may reduce reviewer workload, improve consistency, and strengthen research integrity when used as a decision-support system. However, significant concerns remain regarding algorithmic bias, lack of transparency, confidentiality of unpublished manuscripts, overreliance on automated outputs, and the potential erosion of human judgment. The review synthesizes emerging models of human–AI collaboration, including human-in-the-loop and explainable AI approaches, and discusses their implications for editors, reviewers, authors, and publishers.
Conclusions: AI has the potential to meaningfully augment peer review but should not replace human expertise. Robust empirical research, clear policy frameworks, and continuous ethical oversight are essential to ensure that AI-assisted peer review strengthens, rather than undermines, the credibility and fairness of scholarly publishing. Recommendations for responsible and ethical use of AI in peer review are proposed, emphasizing transparency, accountability, bias monitoring, data governance, and alignment with international editorial standards.
