Introduction

Peer review is a key component to the success of scientific articles. High-quality peer review ensures reliable information, improves the efficiency of communication, and facilitates the interpretation of findings for better applicability of the data presented in studies. Recently, with the increasing availability of artificial intelligence (AI) tools, especially large language models (LLMs), several scientific journals have published policies and guidelines regarding their use in the peer review process. LLMs are generative AI tools capable of producing text, figures, and other forms of content based on the datasets on which they have been trained. Their use in scientific publications has gained prominence in improving the quality of scientific writing, translating texts, reformatting references, and supporting peer review. There are still no clear guidelines on the use of LLMs in peer review. In 2024, Li et al. reviewed the editorial practices of 100 high-impact medical journals and found that 78% had clear guidelines on the use of AI in peer review. Among these, 59% (46 journals) explicitly prohibited the use of AI in generating reports, whereas 32 journals (41%) allow its use in a limited manner, provided that confidentiality was maintained and copyright protected. The objective of this article was to present a review on this topic and to propose a rationale for implementing AI in accordance with best practices in ethics and scientific integrity.

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