From Images to Actions: Opportunities for Artificial Intelligence in Radiology
2017; Radiological Society of North America; Volume: 285; Issue: 3 Linguagem: Inglês
10.1148/radiol.2017171734
ISSN1527-1315
Autores Tópico(s)Radiomics and Machine Learning in Medical Imaging
ResumoHomeRadiologyVol. 285, No. 3 PreviousNext Reviews and CommentaryEditorialsFrom Images to Actions: Opportunities for Artificial Intelligence in RadiologyCharles E. Kahn, Jr Charles E. Kahn, Jr Author AffiliationsFrom the Department of Radiology, Institute for Biomedical Informatics, and Leonard Davis Institute of Health Economics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.Address correspondence to the author (e-mail: [email protected]).Charles E. Kahn, Jr Published Online:Nov 20 2017https://doi.org/10.1148/radiol.2017171734MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In AbstractRadiologists play an important role in guiding, vetting, and incorporating artificial intelligence systems into clinical practice.References1. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. RadioGraphics 2017;37(2):505–515. Link, Google Scholar2. 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Crossref, Medline, Google ScholarArticle HistoryReceived July 24, 2017; final version accepted July 25.Published online: Nov 20 2017Published in print: Dec 2017 FiguresReferencesRelatedDetailsCited ByCardiovascular and Coronary Artery ImagingShan WeiChen, Shir LiWang, Theam FooNg, HaidiIbrahim2023Convolutional neural network for detecting rib fractures on chest radiographs: a feasibility studyJiangfenWu, NijunLiu, XianjunLi, QianruiFan, ZhihaoLi, JinShang, FeiWang, BoweiChen, YuanwangShen, PanCao, ZheLiu, MiaolingLi, JiayaoQian, JianYang, QinliSun2023 | BMC Medical Imaging, Vol. 23, No. 1Hacking and Artificial Intelligence in Radiology: Basic Principles of Data Integrity and SecurityE. 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