High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content
University of Massachusetts Lowell · University of the Cumberlands · +1 more institution
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…
Citation impact
- FWCI
- 9.01
- Percentile
- 100%
- References
- 12
Authors
4- MBMehul BhattacharyyaCorresponding
University of Massachusetts Lowell, University of the Cumberlands, Foster-Miller (United States)
- VMValerie M. Miller
University of Massachusetts Lowell, University of the Cumberlands, Foster-Miller (United States)
- DBDebjani Bhattacharyya
University of Massachusetts Lowell, University of the Cumberlands, Foster-Miller (United States)
- LELarry E. Miller
University of Massachusetts Lowell, University of the Cumberlands, Foster-Miller (United States)
Topics & keywords
- Directory
- Medicine
- Information retrieval
- Medical literature
- Computer science
- Natural language processing
- Pathology
- Quality Education