{"title":"正在进行和计划进行的人工智能医学随机对照试验:对 Clinicaltrials.gov 注册数据的分析","authors":"mattia andreoletti, Berkay Senkalfa, Alessandro Blasimme","doi":"10.1101/2024.07.09.24310133","DOIUrl":null,"url":null,"abstract":"The integration of Artificial Intelligence (AI) technologies into clinical practice holds significant promise for revolutionizing healthcare. However, the realization of this potential requires rigorous evaluation and validation of AI applications to ensure their safety, efficacy, and clinical significance. Despite increasing awareness of the need for robust testing, the majority of AI-related Randomized Controlled Trials (RCTs) so far have exhibited notable limitations, impeding the generalizability and proper integration of their findings into clinical settings. To understand whether the field is progressing towards more robust testing, we conducted an analysis of the registration data of ongoing and planned RCTs of AI in medicine available in the Clinicaltrials.gov database. Our analysis highlights several key trends and challenges. Effectively addressing these challenges is essential for advancing the field of medical AI and ensuring its successful integration into clinical practice.","PeriodicalId":501454,"journal":{"name":"medRxiv - Health Informatics","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ongoing and planned Randomized Controlled Trials of AI in medicine: An analysis of Clinicaltrials.gov registration data\",\"authors\":\"mattia andreoletti, Berkay Senkalfa, Alessandro Blasimme\",\"doi\":\"10.1101/2024.07.09.24310133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The integration of Artificial Intelligence (AI) technologies into clinical practice holds significant promise for revolutionizing healthcare. However, the realization of this potential requires rigorous evaluation and validation of AI applications to ensure their safety, efficacy, and clinical significance. Despite increasing awareness of the need for robust testing, the majority of AI-related Randomized Controlled Trials (RCTs) so far have exhibited notable limitations, impeding the generalizability and proper integration of their findings into clinical settings. To understand whether the field is progressing towards more robust testing, we conducted an analysis of the registration data of ongoing and planned RCTs of AI in medicine available in the Clinicaltrials.gov database. Our analysis highlights several key trends and challenges. Effectively addressing these challenges is essential for advancing the field of medical AI and ensuring its successful integration into clinical practice.\",\"PeriodicalId\":501454,\"journal\":{\"name\":\"medRxiv - Health Informatics\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-09\",\"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/10.1101/2024.07.09.24310133\",\"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/10.1101/2024.07.09.24310133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ongoing and planned Randomized Controlled Trials of AI in medicine: An analysis of Clinicaltrials.gov registration data
The integration of Artificial Intelligence (AI) technologies into clinical practice holds significant promise for revolutionizing healthcare. However, the realization of this potential requires rigorous evaluation and validation of AI applications to ensure their safety, efficacy, and clinical significance. Despite increasing awareness of the need for robust testing, the majority of AI-related Randomized Controlled Trials (RCTs) so far have exhibited notable limitations, impeding the generalizability and proper integration of their findings into clinical settings. To understand whether the field is progressing towards more robust testing, we conducted an analysis of the registration data of ongoing and planned RCTs of AI in medicine available in the Clinicaltrials.gov database. Our analysis highlights several key trends and challenges. Effectively addressing these challenges is essential for advancing the field of medical AI and ensuring its successful integration into clinical practice.