Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Danton CharFrom the Department of Anesthesiology, Division of Pediatric Cardiac Anesthesia (D.S.C.), the Center for Biomedical Ethics (D.S.C., D.M.), and the Center for Biomedical Informatics Research (N.S.), Stanford University School of Medicine, Stanford, CANigam H. ShahFrom the Department of Anesthesiology, Division of Pediatric Cardiac Anesthesia (D.S.C.), the Center for Biomedical Ethics (D.S.C., D.M.), and the Center for Biomedical Informatics Research (N.S.), Stanford University School of Medicine, Stanford, CADavid MagnusFrom the Department of Anesthesiology, Division of Pediatric Cardiac Anesthesia (D.S.C.), the Center for Biomedical Ethics (D.S.C., D.M.), and the Center for Biomedical Informatics Research (N.S.), Stanford University School of Medicine, Stanford, CA
2018en
ABI
Аннотация
We need to consider the ethical challenges inherent in implementing machine learning in health care if its benefits are to be realized. Some of these challenges are straightforward, whereas others have less obvious risks but raise broader ethical concerns.
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