Endorsement of artificial intelligence guidelines across leading endocrinology journals: a cross-sectional analysis

Abstract

Background With expanding applications of artificial intelligence (AI) within the research pipeline of endocrinology, it is essential that journals uphold explicit AI usage policies that maintain the rigor and integrity of published research. In this review, we aim to evaluate current AI policies of leading endocrinology journals to assess the current landscape of research and the implications of its progression. Methods We conducted a cross-sectional review of the top endocrinology journals using the SCImago Journal Ranking (SJR) database. From November 2024 to July 2025, we reviewed AI usage guidelines from publicly available Instructions for Authors, including authorship, manuscript writing, and content/image generation. We also assessed whether journals endorsed AI-specific reporting guidelines (e.g., CONSORT-AI, SPIRIT-AI). Data were extracted independently and in duplicate using a standardized form. Reproducibility was supported through protocol registration on Open Science Framework. Results Of the top 100 endocrinology journals, 84.0% (84/100) mentioned AI in their Instructions for Authors and 79.0% (79/100) required disclosure of AI use during submission. Although no journals (0/100) permitted AI tools for authorship, 64.0% (64/100) allowed its use in manuscript writing, 22.0% (22/100) for content generation, and 50.0% (50/100) for image generation. Despite these guidelines, only one (1.0%; 1/100) journal required a specific reporting guideline, and very few endorsed AI statements by the IMCJE (9/100), COPE (12/100), or WAME (0/100). No statistically significant correlations were identified between AI usage policies and SJR or impact factor. Conclusion Many leading endocrinology journals have addressed AI use; however, their policies remain incomprehensive. It is critical that publishers and their journals establish explicit guidelines regarding the use of AI tools to promote transparent, reproducible, and reliable research.

Ähnliche Arbeiten