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本文引用的文献

1
Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.同时使用人工智能提高数字乳腺断层合成的准确性和效率。
Radiol Artif Intell. 2019 Jul 31;1(4):e180096. doi: 10.1148/ryai.2019180096.
2
New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence.新前沿：人工智能时代下乳腺成像计算机辅助诊断的最新进展。
AJR Am J Roentgenol. 2019 Feb;212(2):300-307. doi: 10.2214/AJR.18.20392.
3
Shining Light Into the Black Box of Machine Learning.照亮机器学习的黑匣子
J Natl Cancer Inst. 2019 Sep 1;111(9):877-879. doi: 10.1093/jnci/djy226.
4
Breast Cancer Screening Using Tomosynthesis or Mammography: A Meta-analysis of Cancer Detection and Recall.基于体层合成或乳腺 X 线摄影的乳腺癌筛查：癌症检出和召回的荟萃分析。
J Natl Cancer Inst. 2018 Sep 1;110(9):942-949. doi: 10.1093/jnci/djy121.
5
A Systematic Review of Fatigue in Radiology: Is It a Problem?放射科疲劳问题的系统评价：这是一个问题吗？
AJR Am J Roentgenol. 2018 Apr;210(4):799-806. doi: 10.2214/AJR.17.18613. Epub 2018 Feb 15.
6
Radiology's value chain.放射学的价值链。
Radiology. 2012 Apr;263(1):243-52. doi: 10.1148/radiol.12110227.
7
Computer-aided detection (CAD) in mammography: does it help the junior or the senior radiologist?乳腺钼靶摄影中的计算机辅助检测（CAD）：它对初级放射科医生还是高级放射科医生有帮助？
Eur J Radiol. 2005 Apr;54(1):90-6. doi: 10.1016/j.ejrad.2004.11.021.
8
A method of comparing the areas under receiver operating characteristic curves derived from the same cases.一种比较源自相同病例的受试者工作特征曲线下面积的方法。
Radiology. 1983 Sep;148(3):839-43. doi: 10.1148/radiology.148.3.6878708.

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.

DOI:10.1148/ryai.2019190107
PMID:33937798
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8017416/
Abstract
摘要