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Reimagining Peer Review in the Artificial Intelligence (AI) Era
Peer review is fundamental to ensuring scientific rigor, credibility, and trust in scholarly publishing. It is a critical quality control mechanism aimed at improving manuscripts and safeguarding the integrity of the scholarly records. However, this cornerstone of scholarlyquality andintegrity is under growing strain. Rising submission volumes, reviewer fatigue, and increasing demands for faster editorial decisions have placed considerable stress on traditional peer‑review models.At the same time, artificial intelligence (AI), particularly large language models, has emerged as a potential tool to support peer‑review processes, prompting both optimism and concern. The challenge is no longer whether AI will influence peer review, but how it can be integrated responsibly without eroding trustwithin the scholarly community and among general audiences.
AI in Peer Review
AI is already influencing peer review in several limited but meaningful ways. At the editorial level, AI‑enabled tools are increasingly explored for pre‑review screening and qualitycontrol, including the identification of missing disclosures, checkingadherence to reporting standards and guidelines and detecting plagiarism and potentially retracted or unverifiable references. These applications aim to improve consistency in the enforcement of journal guidelines and policies and to reduce the administrative burden on editors and reviewers, particularly in areas where human oversight is variable.AI also supports language clarity and structural coherence, especially for manuscripts authored by non‑native English speakers. Under human supervision, such assistance may improve readability without altering scientific meaning. Further, when implemented within secure, publisher‑controlled systems,the use of these AI tools aligns with a widely accepted principle that AI should augment peer-review processes, not replace expert human scientific judgment.
Ethical and Procedural Concerns
But despite these potential benefits, the use of AI in peer review raises significant ethical and procedural concerns. Peer review requires contextual interpretation, subject‑matter expertise, and nuanced clinical or scientific judgment - capabilities that currently available AI tools cannot reliably provide. Over‑reliance on AI‑generated evaluations may lead to superficial reviews and diluted accountability in editorial decision‑making. Additionally, the use of external AI systems by reviewers poses serious risks to manuscript confidentiality, particularly when unpublished content is uploaded to platforms where data security cannot be assured.
Guidelines and Policy Frameworks
The International Committee of Medical Journal Editors (ICMJE) has provided guidance to address these concerns in its 2026 recommendations, reinforcing that responsibility for the accuracy, integrity, and originality of scholarly work lies entirely with humans and not AI tools. The ICMJE 2026 recommendations clearly state that AI tools must not be listed as authors and any use of AI must be transparent and appropriately disclosed. Importantly for peer review, the ICMJE cautions that editors and reviewers should not upload submitted manuscripts into AI systems where confidentiality cannot be assured, unless explicit author permission has been obtained. This is in alignment with guidance from the Committee on Publication Ethics (COPE) and major publishers, which emphasizes confidentiality, accountability, transparency, and data protection as non‑negotiable aspects in AI‑assisted editorial workflows. These safeguards are particularly important in the context of increasing concerns about paper mills, fabricated reviews, and large‑scale research misconduct, where weak governance could further undermine trust in the scientific integrity.
Future Directions
Looking ahead, several trends are likely to shape peer review in the AI era. First, clearer and more harmonized journal policies are emerging, defining permissible AI use for authors, reviewers, and editors, with strong emphasis on disclosure and confidentiality. Second, AI adoption is likely to expand primarily on the publisher or journal side, through secure, in‑house systems designed to support integrity checks while protecting unpublished content.Finally, there will be an increased emphasis on developing AI literacy and appropriate training as essential competencies for editors and reviewers. This will supportthe transparent, ethical, and accountable use of AI tools while ensuring that responsibility for editorial decisions and scientific judgment remains firmly with human experts.Rethinking peer review in the AI era, therefore, requires a balanced, governance‑driven approach. Journals should establish clear policies defining permissible AI use for reviewers and editors, grounded in ICMJE and COPE guidance. At the same time, broader structural challengessuch as reviewer workload and lack of formal incentivesmust be addressed to ensure the sustainability of high‑quality peer review.
In conclusion, AI offers real opportunities to strengthen peer review when used responsibly, transparently, and within well‑defined ethical boundaries. The future of peer review does not lie in replacing human expertise, but in the thoughtful integration of AI as a supportive tool. While AI can enhance efficiency and consistency, human judgment, accountability, and trust must continue to underpin scientific communication. Adherence to principles articulated by the ICMJE and related bodies will be essential to preserving the credibility of scholarly publishing in the AI era.
Keywords
Peer review Artificial intelligence Scholarly publishing Large language models Research integrity Editorial workflow COPE guidelines ICMJE recommendations Scientific publishing ethics Manuscript evaluation AI-assisted review Publication ethics
I am an International Society for Medical Publication Professionals-Certified Medical Publication Professional™ (ISMPP-CMPP™ 2020) with over 18 years of experience spanning academia and the pharmaceutical industry. Throughout my career, I have supported academic authors, scientists, editors, and peer reviewers across the entire publication lifecycle—from manuscript development to submission—ensuring the delivery of high-quality, compliant scientific content. With more than one and a half decades of specialized experience in medical communications, I have worked with leading pharmaceutical companies and healthcare solution providers, contributing to the successful execution of a wide range of publication and communication projects. In addition to my core publication expertise, I have been actively involved in project scoping, budgeting, and proposal development. As a culturally adaptable and collaborative professional, I am passionate about advancing best practices in science communication by mentoring early-career professionals and enhancing awareness of ethical publishing practices within the scientific community.
View All Posts by Ramandeep SinghDisclaimer
The views and opinions expressed in this article are those of the author(s) and do not necessarily reflect the official policy or position of their affiliated institutions, the Asian Council of Science Editors (ACSE), or the Editor’s Café editorial team.
