Reem Agbareia, Mahmud Omar, Shelly Soffer, Benjamin S Glicksberg, Girish N Nadkarni, Eyal Klang
{"title":"用于医学诊断的 LLM 中的视觉-文本整合:定量分析","authors":"Reem Agbareia, Mahmud Omar, Shelly Soffer, Benjamin S Glicksberg, Girish N Nadkarni, Eyal Klang","doi":"10.1101/2024.08.31.24312878","DOIUrl":null,"url":null,"abstract":"<strong>Background and Aim</strong> Visual data from images is essential for many medical diagnoses. This study evaluates the performance of multimodal Large Language Models (LLMs) in integrating textual and visual information for diagnostic purposes.","PeriodicalId":501454,"journal":{"name":"medRxiv - Health Informatics","volume":"119 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visual-Textual Integration in LLMs for Medical Diagnosis: A Quantitative Analysis\",\"authors\":\"Reem Agbareia, Mahmud Omar, Shelly Soffer, Benjamin S Glicksberg, Girish N Nadkarni, Eyal Klang\",\"doi\":\"10.1101/2024.08.31.24312878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Background and Aim</strong> Visual data from images is essential for many medical diagnoses. This study evaluates the performance of multimodal Large Language Models (LLMs) in integrating textual and visual information for diagnostic purposes.\",\"PeriodicalId\":501454,\"journal\":{\"name\":\"medRxiv - Health Informatics\",\"volume\":\"119 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 - Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.08.31.24312878\",\"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 - Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.31.24312878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual-Textual Integration in LLMs for Medical Diagnosis: A Quantitative Analysis
Background and Aim Visual data from images is essential for many medical diagnoses. This study evaluates the performance of multimodal Large Language Models (LLMs) in integrating textual and visual information for diagnostic purposes.