Better medicine through machine learning: What’s real, and what’s artificial?

Machine Learning Special Issue Guest Editors Suchi Saria, Atul Butte, and Aziz Sheikh cut through the hyperbole with an accessible and accurate portrayal of the forefront of machine learning in clinical translation.

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Bibliographic Details
Published in	PLoS medicine Vol. 15; no. 12; p. e1002721
Main Authors	Saria, Suchi, Butte, Atul, Sheikh, Aziz
Format	Journal Article
Language	English
Published	United States Public Library of Science 31.12.2018 Public Library of Science (PLoS)
Subjects	Abbreviations Algorithms Artificial intelligence Artificial Intelligence - standards Artificial Intelligence - trends Asthma Atropine Biotechnology Clinical medicine Computer and Information Sciences Computer applications Decision making Deep learning Diabetes Diabetic retinopathy Diagnosis, Computer-Assisted - standards Diagnosis, Computer-Assisted - trends Electronic medical records FDA approval Food allergies Food hypersensitivity Health policy Humans Immunoglobulin E Informatics Internet Learning Learning algorithms Logos Machine learning Machine Learning - standards Machine Learning - trends Medical electronics Medical personnel Medical records Medicine Medicine - standards Medicine - trends Medicine and Health Sciences Patients People and Places Physical Sciences Quality control Regulatory agencies Research and Analysis Methods Safety engineering Sepsis Side effects Software Technology application Tomography San Francisco California California United Kingdom > UK United States > US
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ISSN	1549-1676 1549-1277 1549-1676
DOI	10.1371/journal.pmed.1002721

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Summary:	Machine Learning Special Issue Guest Editors Suchi Saria, Atul Butte, and Aziz Sheikh cut through the hyperbole with an accessible and accurate portrayal of the forefront of machine learning in clinical translation.
Bibliography:	SourceType-Scholarly Journals-1 content type line 14 ObjectType-Editorial-2 ObjectType-Commentary-1 content type line 23 We have read the journal's policy and the authors of this manuscript have the following competing interests: SS has grants from Gordon and Betty Moore Foundation, the National Science Foundation, the Defense Advanced Research Projects Agency (DARPA), and the American Heart Association. She is the scientific founder and/or advisory board member for Bayesian Health and PatientPing. Bayesian Health develops real-time decision-making software for health systems. PatientPing offers software for real-time notifications about patient admission and discharge transfer status. She has received honoraria for talks from a number of biotechnology, research, and healthtech companies. She has no direct ties to the companies mentioned in this Editorial. AB is a scientific founder and/or advisory board member for NuMedii and Personalis. He has received honoraria for talks, has consulted, and has corporate relationships with a number of biotechnology, research, healthcare, and financial companies, and some of his students have founded biotechnology and healthcare companies. He has no direct ties to the companies mentioned in this Editorial. AS has relevant research grants from Chief Scientist’s Office of the Scottish Government, Department of Health, Farr Institute, Health Data Research UK, National Institute of Health Research, and NHS England, and he chairs the World Innovation Summit for Health (WISH) Forum on Data Science and Artificial Intelligence. SS, AB, and AS served as Guest Editors on PLOS Medicine’s Special Issue on Machine Learning in Health and Biomedicine.
ISSN:	1549-1676 1549-1277 1549-1676
DOI:	10.1371/journal.pmed.1002721