The rapid advancements in artificial intelligence (AI) technology in recent years have led to its integration into biomedical publishing. However, the extent to which AI has contributed to developing biomedical literature is unclear. This study aimed to identify trends in AI-generated content within peer-reviewed biomedical literature. We first tested the sensitivity and specificity of commercially available AI-detection software (Originality.AI, Collingwood, Ontario, Canada). Next, we conducted a MEDLINE (Medical Literature Analysis and Retrieval System Online) search to identify randomized controlled trials with available abstracts indexed between January 2020 and March 2023. We randomly selected 30 abstracts per quarter during this period and pasted the abstracts into the AI detection software to determine the probability of AI-generated content. The software yielded 100% sensitivity, 95% specificity, and excellent overall discriminatory ability with an area under the receiving operating curve of 97.6%. Among the 390 MEDLINE-indexed abstracts included in the analysis, the prevalence with a high probability (≥ 90%) of AI-generated text increased during the study period from 21.7% to 36.7% (p=0.01) based on a chi-square test for trend. The increasing prevalence of AI-generated text during the study period was also observed in various sensitivity analyses using AI probability thresholds ranging from 50% to 99% (all p≤0.01). The results of this study suggest that the prevalence of AI-assisted publishing in peer-reviewed journals has been increasing in recent years, even before the widespread adoption of ChatGPT (OpenAI, San Francisco, California, United States) and similar tools. The extent to which natural writing characteristics of the authors, utilization of common AI-powered applications, and introduction of AI elements during the post-acceptance publication phase influence AI detection scores warrants further study.
Keywords: artificial intelligence; chatgpt; large language model; machine learning; references.
Copyright © 2023, Miller et al.