Safeguards for the use of artificial intelligence and machine learning in global health.
作者信息
Paul Amy K, Schaefer Merrick
机构信息
United States Agency for International Development, 1300 Pennsylvania Ave NW, Washington, DC, 20004, United States of America (USA).
U.S. Global Development Lab, USAID, Washington, DC, USA.
出版信息
Bull World Health Organ. 2020 Apr 1;98(4):282-284. doi: 10.2471/BLT.19.237099. Epub 2020 Jan 27.
Abstract
摘要
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2
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3
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