Background A thorough and in-depth examination of generative artificial intelligence (GAI) usage guidelines in medical journals will inform potential gaps and promote proper GAI usage in scholarly publishing. This study aims to examine the provision and specificity of GAI usage guidelines and their relationships with journal characteristics. Methods From the SCImago Journal Rank (SJR) list for medicine in 2022, we selected 98 journals as top journals to represent highly indexed journals and 144 as whole-spectrum sample journals to represent all medical journals. We examined their GAI usage guidelines for scholarly publishing between December 2023 and January 2024. Results Compared to whole-spectrum sample journals, the top journals were more likely to provide author guidelines (64.3% vs. 27.8%) and reviewer guidelines (11.2% vs. 0.0%) as well as refer to external guidelines (85.7% vs 74.3%). Probit models showed that SJR score or region was not associated with the provision of these guidelines among top journals. However, among whole-spectrum sample journals, SJR score was positively associated with the provision of author guidelines (0.85, 95% CI 0.49 to 1.25) and references to external guidelines (2.01, 95% CI 1.24 to 3.65). Liner models showed that SJR score was positively associated with the specificity level of author and reviewer guidelines among whole-spectrum sample journals (1.21, 95% CI 0.72 to 1.70), and no such pattern was observed among top journals. Conclusions The provision of GAI usage guidelines is limited across medical journals, especially for reviewer guidelines. The lack of specificity and consistency in existing guidelines highlights areas deserving improvement. These findings suggest that immediate attention is needed to guide GAI usage in scholarly publishing in medical journals.

The authors have declared no competing interest.

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All data relevant to the study are included in the article or included as supplementary materials. Full data and analytical code are available from the corresponding author at chihuali@umich.edu.