Key points are not available for this paper at this time.
The rapid advancement of artificial intelligence (AI) has transformed various aspects of scientific research, including academic publishing and peer review. In recent years, AI tools such as large language models have demonstrated their capability to streamline numerous tasks traditionally handled by human editors and reviewers. These applications range from automated language and grammar checks to plagiarism detection, format compliance, and even preliminary assessment of research significance. While AI substantially benefits the efficiency and accuracy of academic processes, its integration raises critical ethical and methodological questions, particularly in peer review. AI lacks the subtle understanding of complex scientific content that human expertise provides, posing challenges in evaluating research novelty and significance. Additionally, there are risks associated with over-reliance on AI, potential biases in AI algorithms, and ethical concerns related to transparency, accountability, and data privacy. This review evaluates the perspectives within the scientific community on integrating AI in peer review and academic publishing. By exploring both AI's potential benefits and limitations, we aim to offer practical recommendations that ensure AI is used as a supportive tool, supporting but not replacing human expertise. Such guidelines are essential for preserving the integrity and quality of academic work while benefiting from AI's efficiencies in editorial processes.
Doskaliuk et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: