{"title":"大型语言模型在肺-RADS 相关问题上的性能比较。","authors":"Eren Çamur, Turay Cesur, Yasin Celal Güneş","doi":"10.1200/GO.24.00200","DOIUrl":null,"url":null,"abstract":"<p><p>This study evaluates LLM integration in interpreting Lung-RADS for lung cancer screening, highlighting their innovative role in enhancing radiological practice. Our findings reveal that Claude 3 Opus and Perplexity achieved a 96% accuracy rate, outperforming other models.</p>","PeriodicalId":14806,"journal":{"name":"JCO Global Oncology","volume":"10 ","pages":"e2400200"},"PeriodicalIF":3.2000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of Performance of Large Language Models on Lung-RADS Related Questions.\",\"authors\":\"Eren Çamur, Turay Cesur, Yasin Celal Güneş\",\"doi\":\"10.1200/GO.24.00200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study evaluates LLM integration in interpreting Lung-RADS for lung cancer screening, highlighting their innovative role in enhancing radiological practice. Our findings reveal that Claude 3 Opus and Perplexity achieved a 96% accuracy rate, outperforming other models.</p>\",\"PeriodicalId\":14806,\"journal\":{\"name\":\"JCO Global Oncology\",\"volume\":\"10 \",\"pages\":\"e2400200\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JCO Global Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1200/GO.24.00200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCO Global Oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1200/GO.24.00200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Comparison of Performance of Large Language Models on Lung-RADS Related Questions.
This study evaluates LLM integration in interpreting Lung-RADS for lung cancer screening, highlighting their innovative role in enhancing radiological practice. Our findings reveal that Claude 3 Opus and Perplexity achieved a 96% accuracy rate, outperforming other models.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
