{"title":"Prompt Engineering Strategies Improve the Diagnostic Accuracy of GPT-4 Turbo in Neuroradiology Cases","authors":"Akihiko Wada, Toshiaki Akashi, George Shih, Akifumi Hagiwara, Mitsuo Nishizawa, Yayoi Hayakawa, Junko Kikuta, Keigo Shimoji, Katsuhiro Sano, Koji Kamagata, Atsushi Nakanishi, Shigeki Aoki","doi":"10.1101/2024.04.29.24306583","DOIUrl":null,"url":null,"abstract":"<strong>Background</strong> Large language models (LLMs) like GPT-4 demonstrate promising capabilities in medical image analysis, but their practical utility is hindered by substantial misdiagnosis rates ranging from 30-50%.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Radiology and Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.04.29.24306583","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
Background Large language models (LLMs) like GPT-4 demonstrate promising capabilities in medical image analysis, but their practical utility is hindered by substantial misdiagnosis rates ranging from 30-50%.
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