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Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions by Jocelyn Gravel; Madeleine D’Amours-Gravel; Esli Osmanlliu is a scholarly article available to read on EtoBox.

What is Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions about?

Background: ChatGPT have gained public notoriety and recently supported manuscript preparation. Our objective was to evaluate the quality of the answers and the references provided by ChatGPT for medical questions. Methods: Three researchers asked ChatGPT a total of 20 medical questions and prompted it to provide the corresponding references. The responses were evaluated for quality of content by medical experts using a verbal numeric scale going from 0 to 100%. These experts were the corresponding author of the 20 articles from where the medical questions were derived. We planned to evaluate three references per response for their pertinence, but this was amended based on preliminary results showing that most references provided by ChatGPT were fabricated. Results: ChatGPT provided responses varying between 53 and 244 words long and reported two to seven references per answer. Seventeen of the 20 invited raters provided feedback. The raters reported limited quality of the responses with a median score of 60% (1 st and 3 rd quartile: 50% and 85%). Additionally, they identified major (n=5) and minor (n=7) factual errors among the 17 evaluated responses. Of the 59 references evaluate

Author

Jocelyn Gravel; Madeleine D’Amours-Gravel; Esli Osmanlliu

Publisher

Cold Spring Harbor Laboratory

Published

2023

Language

EN