High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content

Abstract

Background The availability of large language models such as Chat Generative Pre-trained Transformer (ChatGPT, OpenAI) has enabled individuals from diverse backgrounds to access medical information. However, concerns exist about the accuracy of ChatGPT responses and the references used to generate medical content. Methods This observational study investigated the authenticity and accuracy of references in medical articles generated by ChatGPT. ChatGPT-3.5 generated 30 short medical papers, each with at least three references, based on standardized prompts encompassing various topics and therapeutic areas. Reference authenticity and accuracy were verified by searching Medline, Google Scholar, and the Directory of Open Access Journals. The authenticity and accuracy of individual ChatGPT-generated reference elements were also determined. Results Overall, 115 references were generated by ChatGPT, with a mean of 3.8±1.1 per paper. Among these references, 47% were fabricated, 46% were authentic but inaccurate, and only 7% were authentic and accurate. The likelihood of fabricated references significantly differed based on prompt variations; yet the frequency of authentic and accurate references remained low in all cases. Among the seven components evaluated for each reference, an incorrect PMID number was most common, listed in 93% of papers. Incorrect volume (64%), page numbers (64%), and year of publication (60%) were the next most frequent errors. The mean number of inaccurate components was 4.3±2.8 out of seven per reference. Conclusions The findings of this study emphasize the need for caution when seeking medical information on ChatGPT since most of the references provided were found to be fabricated or inaccurate. Individuals are advised to verify medical information from reliable sources and avoid relying solely on artificial intelligence-generated content.

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