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本文引用的文献

1
A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.用于数字乳腺断层合成图像中肿块和结构扭曲检测的数据集合和深度学习算法。
JAMA Netw Open. 2021 Aug 2;4(8):e2119100. doi: 10.1001/jamanetworkopen.2021.19100.
2
Rethinking Patient Consent in the Era of Artificial Intelligence and Big Data.人工智能与大数据时代下对患者同意书的重新思考。
J Am Coll Radiol. 2021 Jan;18(1 Pt B):180-184. doi: 10.1016/j.jacr.2020.09.022.
3
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms.商业人工智能算法独立评估筛查性乳房 X 光照片的外部评估。
JAMA Oncol. 2020 Oct 1;6(10):1581-1588. doi: 10.1001/jamaoncol.2020.3321.
4
Data Sharing For Precision Medicine: Policy Lessons And Future Directions.精准医学的数据共享：政策经验教训与未来方向。
Health Aff (Millwood). 2018 May;37(5):702-709. doi: 10.1377/hlthaff.2017.1558.

Data Quality, Data Sharing, and Moving Artificial Intelligence Forward.

作者信息

Elmore Joann G, Lee Christoph I

机构信息

Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California.

Department of Radiology, University of Washington School of Medicine, Seattle.

出版信息

JAMA Netw Open. 2021 Aug 2;4(8):e2119345. doi: 10.1001/jamanetworkopen.2021.19345.

DOI:10.1001/jamanetworkopen.2021.19345
PMID:34398208
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8612009/
Abstract
摘要