{"title":"结构化临床推理提示增强了 LLM 在 \"诊断请回答 \"案例中的诊断能力","authors":"Yuki Sonoda, Ryo Kurokawa, Akifumi Hagiwara, Yusuke Asari, Takahiro Fukushima, Jun Kanzawa, Wataru Gonoi, Osamu Abe","doi":"10.1101/2024.09.01.24312894","DOIUrl":null,"url":null,"abstract":"<strong>Background</strong> Large Language Models (LLMs) show promise in medical diagnosis, but their performance varies with prompting. Recent studies suggest that modifying prompts may enhance diagnostic capabilities.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Structured Clinical Reasoning Prompt Enhances LLM’s Diagnostic Capabilities in Diagnosis Please Quiz Cases\",\"authors\":\"Yuki Sonoda, Ryo Kurokawa, Akifumi Hagiwara, Yusuke Asari, Takahiro Fukushima, Jun Kanzawa, Wataru Gonoi, Osamu Abe\",\"doi\":\"10.1101/2024.09.01.24312894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Background</strong> Large Language Models (LLMs) show promise in medical diagnosis, but their performance varies with prompting. Recent studies suggest that modifying prompts may enhance diagnostic capabilities.\",\"PeriodicalId\":501358,\"journal\":{\"name\":\"medRxiv - Radiology and Imaging\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-03\",\"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/10.1101/2024.09.01.24312894\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Radiology and Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.01.24312894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Background Large Language Models (LLMs) show promise in medical diagnosis, but their performance varies with prompting. Recent studies suggest that modifying prompts may enhance diagnostic capabilities.
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