Generative AI-assisted Peer Review in Medical Publications: Opportunities Or Trap (Preprint)

Abstract

<sec> <title>UNSTRUCTURED</title> With the exponential growth in the number of research papers and the proliferation of preprint servers, ensuring high-quality peer review has become a significant challenge, especially in the medical field. The surge in submissions has led to a shortage of qualified reviewers, slowing down the peer review process. The repeated review of rejected manuscripts not only increases costs but may also stifle research innovation, raising concerns about the efficiency, fairness, and effectiveness of the review process. Therefore, innovative solutions are urgently needed. Recent advancements in generative artificial intelligence (GenAI), such as ChatGPT, have demonstrated exceptional capabilities in feature learning and textual expression, allowing them to identify complex relationships within data without relying on pre-existing assumptions. GenAI present an opportunity to enhance semi-automated peer review systems, potentially addressing the current limitations in the peer review process and improving the efficiency and quality of medical publications. This viewpoint highlights the potential benefits and challenges of integrating GenAI into the peer review and identifies the key issues that need to be addressed. </sec>

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