Marta N. Flory MD (Clinical Assistant Professor), Sandy Napel PhD (Professor of Radiology and, by courtesy, of Medicine (Informatics) and Electrical Engineering), Emily B. Tsai MD (Clinical Associate Professor)
{"title":"放射学中的人工智能：机遇与挑战。","authors":"Marta N. Flory MD (Clinical Assistant Professor), Sandy Napel PhD (Professor of Radiology and, by courtesy, of Medicine (Informatics) and Electrical Engineering), Emily B. Tsai MD (Clinical Associate Professor)","doi":"10.1053/j.sult.2024.02.004","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence’s (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process – from algorithm initiation and design to development and implementation – to maximize benefit and minimize harm that can be enabled by this technology.</p></div>","PeriodicalId":49541,"journal":{"name":"Seminars in Ultrasound Ct and Mri","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence in Radiology: Opportunities and Challenges\",\"authors\":\"Marta N. Flory MD (Clinical Assistant Professor), Sandy Napel PhD (Professor of Radiology and, by courtesy, of Medicine (Informatics) and Electrical Engineering), Emily B. Tsai MD (Clinical Associate Professor)\",\"doi\":\"10.1053/j.sult.2024.02.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Artificial intelligence’s (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process – from algorithm initiation and design to development and implementation – to maximize benefit and minimize harm that can be enabled by this technology.</p></div>\",\"PeriodicalId\":49541,\"journal\":{\"name\":\"Seminars in Ultrasound Ct and Mri\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seminars in Ultrasound Ct and Mri\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0887217124000052\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seminars in Ultrasound Ct and Mri","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0887217124000052","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Artificial Intelligence in Radiology: Opportunities and Challenges
Artificial intelligence’s (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process – from algorithm initiation and design to development and implementation – to maximize benefit and minimize harm that can be enabled by this technology.
期刊介绍：
Seminars in Ultrasound, CT and MRI is directed to all physicians involved in the performance and interpretation of ultrasound, computed tomography, and magnetic resonance imaging procedures. It is a timely source for the publication of new concepts and research findings directly applicable to day-to-day clinical practice. The articles describe the performance of various procedures together with the authors'' approach to problems of interpretation.
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
