Rising Prevalence of Detected AI-Generated Text in Medical Literature: Longitudinal Analysis in Open Access Articles
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
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2026
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| author | Wolfrath, Nathan Patel, Simrin Flitcroft, Madelyn Banerjee, Anjishnu Somai, Melek Crotty, Bradley H. Kothari, Anai N |
| author_facet | Wolfrath, Nathan Patel, Simrin Flitcroft, Madelyn Banerjee, Anjishnu Somai, Melek Crotty, Bradley H. Kothari, Anai N |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_19316 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rising Prevalence of Detected AI-Generated Text in Medical Literature: Longitudinal Analysis in Open Access Articles Wolfrath, Nathan Patel, Simrin Flitcroft, Madelyn Banerjee, Anjishnu Somai, Melek Crotty, Bradley H. Kothari, Anai N Digital Libraries 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. |
| title | Rising Prevalence of Detected AI-Generated Text in Medical Literature: Longitudinal Analysis in Open Access Articles |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2603.19316 |