Using Time as a Measure of Impact for AI Systems: Implications in Breast Screening.
作者信息
Hsu William, Hoyt Anne C
机构信息
Department of Radiological Sciences, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024.
出版信息
Radiol Artif Intell. 2019 Jul 31;1(4):e190107. doi: 10.1148/ryai.2019190107. eCollection 2019 Jul.
Abstract
摘要
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3
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