Meghal Shah, Eric J. Kuo, Jennifer H. Kuo, Shawn Hsu, Catherine McManus, Rachel Liou, James A. Lee, Tejas S. Sathe
{"title":"EndoGPT:基于大语言模型的甲状腺结节管理助手的概念验证","authors":"Meghal Shah, Eric J. Kuo, Jennifer H. Kuo, Shawn Hsu, Catherine McManus, Rachel Liou, James A. Lee, Tejas S. Sathe","doi":"10.1101/2024.05.29.24308002","DOIUrl":null,"url":null,"abstract":"Large language models (LLMs) are increasingly being explored for their potential to simulate clinical reasoning. Here, we demonstrate our initial experience using the GPT-4o LLM along with prompt engineering and knowledge retrieval to develop EndoGPT, a clinical decision support tool for the management of thyroid nodules. In a pilot study of 50 cases, EndoGPT demonstrated an 83% concordance rate with expert surgeons’ assessments and plans. The highest concordance was in diagnosis (93%), followed by the need for an operation (82%) and type of operation (69%). This work suggests that LLM-based assistants may play a useful role in assisting clinicians in the future.","PeriodicalId":501051,"journal":{"name":"medRxiv - Surgery","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EndoGPT: A Proof-of-concept Large Language Model Based Assistant for the Management of Thyroid Nodules\",\"authors\":\"Meghal Shah, Eric J. Kuo, Jennifer H. Kuo, Shawn Hsu, Catherine McManus, Rachel Liou, James A. Lee, Tejas S. Sathe\",\"doi\":\"10.1101/2024.05.29.24308002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Large language models (LLMs) are increasingly being explored for their potential to simulate clinical reasoning. Here, we demonstrate our initial experience using the GPT-4o LLM along with prompt engineering and knowledge retrieval to develop EndoGPT, a clinical decision support tool for the management of thyroid nodules. In a pilot study of 50 cases, EndoGPT demonstrated an 83% concordance rate with expert surgeons’ assessments and plans. The highest concordance was in diagnosis (93%), followed by the need for an operation (82%) and type of operation (69%). This work suggests that LLM-based assistants may play a useful role in assisting clinicians in the future.\",\"PeriodicalId\":501051,\"journal\":{\"name\":\"medRxiv - Surgery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv - Surgery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.05.29.24308002\",\"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 - Surgery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.05.29.24308002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
EndoGPT: A Proof-of-concept Large Language Model Based Assistant for the Management of Thyroid Nodules
Large language models (LLMs) are increasingly being explored for their potential to simulate clinical reasoning. Here, we demonstrate our initial experience using the GPT-4o LLM along with prompt engineering and knowledge retrieval to develop EndoGPT, a clinical decision support tool for the management of thyroid nodules. In a pilot study of 50 cases, EndoGPT demonstrated an 83% concordance rate with expert surgeons’ assessments and plans. The highest concordance was in diagnosis (93%), followed by the need for an operation (82%) and type of operation (69%). This work suggests that LLM-based assistants may play a useful role in assisting clinicians in the future.