Rising Prevalence of Detected AI-Generated Text in Medical Literature: Longitudinal Analysis in Open Access Articles

2026-03-14

AI Summary

This study investigates the emerging trend of detectable AI-generated text in medical literature by conducting a longitudinal analysis of 7,251 open-access articles published in JAMA Network Open between January 2022 and March 2025. Using Originality.AI to identify AI-generated content, the analysis stratifies findings by publication month, article type, and disciplinary field. The results reveal a statistically significant increase in the proportion of articles containing detectable AI-generated text—from 0.0% in January 2022 to 11.3% in March 2025 (P<0.001)—with an overall prevalence of 2.7%. The highest rates were observed in invited commentaries (6.7%), while only 0.2% of all articles disclosed the use of large language models. This work provides the first systematic evidence of rising AI involvement in peer-reviewed medical publications.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Generative artificial intelligence (AI) tools are becoming increasingly used for writing tasks. However, the extent of their use in peer-reviewed medical literature remains unclear. We conducted a longitudinal analysis of all Original Investigations, Research Letters, and Invited Commentaries published in JAMA Network Open from January 2022 through March 2025. The main body text of 7,251 articles was analyzed using a commercial AI-detection tool (Originality.AI) to estimate the probability that manuscripts contained a significant amount of AI-generated content. Articles were analyzed aggregated by month, publication type, and domain. Overall, 195 articles (2.7%) were classified as containing significant AI-generated text. The monthly proportion increased from 0.0% in January 2022 to 11.3% in March 2025, with a significant upward trend over time (P<0.001). Invited Commentaries had the highest proportion of detected AI-generated content (6.7%), followed by Original Investigations (2.2%) and Research Letters (1.4%). There was also significant variation across publication domain (P=0.04). Only 15 articles (0.2%) disclosed large language model use, of which 40.0% were classified as containing AI-generated text. While findings suggest increasing detectable AI-generated content in medical literature, limitations of current detection tools necessitates cautious interpretation.

Problem

• AI-generated text
• medical literature
• longitudinal analysis
• peer-reviewed articles
• AI detection

Innovation

• AI-generated text detection
• longitudinal analysis
• medical literature
• large language models
• academic integrity