AI-Assisted Writing Across Medical Journals: Heterogeneity in Prevalence and Trends

Background: Estimates of generative artificial intelligence (AI) use in the medical literature vary widely, potentially reflecting differences across journals. We estimated changes in the prevalence of AI-generated writing over time using abstracts from 12 general medical journals. Methods: We randomly sampled up to 200 abstracts per journal-year from publications dated January 1, 2020, through August 8, 2026. Abstracts were scored using GPTZero, with journal-specific thresholds calibrated to fix a 1% false-positive rate (FPR) in the pre-AI baseline period (2020-2021). We estimated annual detector-positive proportions and calculated a publication-volume-weighted estimate across sampled journals. Associations with journal impact factor, publication model, and publication volume were examined descriptively. Results: Among 15,268 abstracts, estimated prevalence of AI-generated text increased after 2022, with substantial variation in timing and magnitude across journals. In 2026 through August 8, prevalence estimates ranged from 5.7% (95% CI, 2.1%-10.0%) in the New England Journal of Medicine to 83.5% (95% CI, 77.1%-87.5%) in Frontiers in Medicine. The publication-volume-weighted estimate across sampled journals was 66.7% (95% CI, 64.0%-69.2%), and trends were insensitive to specification of false-positive rate in baseline data. The proportion of abstracts showing signs of AI-generated text was lower in journals with higher impact factors and higher in fully open-access journals. Conclusions: Estimated prevalence of AI-generated text increased substantially across all sampled medical journals, with marked between-journal differences associated with journal impact factor and publication model. These findings support development of standardized, transparent AI reporting in medical publishing.