{"title":"Ubie 症状检查器：临床小故事模拟研究","authors":"N. Kenji Taylor, Takashi Nishibayashi","doi":"10.1101/2024.08.29.24312810","DOIUrl":null,"url":null,"abstract":"<strong>Background</strong> AI-driven symptom checkers (SC) are increasingly adopted in healthcare for their potential to provide users with accessible and immediate preliminary health education. These tools, powered by advanced artificial intelligence algorithms, assist patients in quickly assessing their symptoms. Previous studies using clinical vignette approaches have evaluated SC accuracy, highlighting both strengths and areas for improvement.","PeriodicalId":501454,"journal":{"name":"medRxiv - Health Informatics","volume":"53 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ubie Symptom Checker: A Clinical Vignette Simulation Study\",\"authors\":\"N. Kenji Taylor, Takashi Nishibayashi\",\"doi\":\"10.1101/2024.08.29.24312810\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Background</strong> AI-driven symptom checkers (SC) are increasingly adopted in healthcare for their potential to provide users with accessible and immediate preliminary health education. These tools, powered by advanced artificial intelligence algorithms, assist patients in quickly assessing their symptoms. Previous studies using clinical vignette approaches have evaluated SC accuracy, highlighting both strengths and areas for improvement.\",\"PeriodicalId\":501454,\"journal\":{\"name\":\"medRxiv - Health Informatics\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-31\",\"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/10.1101/2024.08.29.24312810\",\"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/10.1101/2024.08.29.24312810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ubie Symptom Checker: A Clinical Vignette Simulation Study
Background AI-driven symptom checkers (SC) are increasingly adopted in healthcare for their potential to provide users with accessible and immediate preliminary health education. These tools, powered by advanced artificial intelligence algorithms, assist patients in quickly assessing their symptoms. Previous studies using clinical vignette approaches have evaluated SC accuracy, highlighting both strengths and areas for improvement.
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