Clinical applications of machine learning algorithms: beyond the black box

To maximise the clinical benefits of machine learning algorithms, we need to rethink our approach to explanation, argue David Watson and colleagues

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Bibliographic Details
Published in	BMJ (Online) Vol. 364; p. l886
Main Authors	Watson, David S, Krutzinna, Jenny, Bruce, Ian N, Griffiths, Christopher EM, McInnes, Iain B, Barnes, Michael R, Floridi, Luciano
Format	Journal Article
Language	English
Published	England BMJ Publishing Group LTD 12.03.2019
Subjects	Algorithms Artificial intelligence Attitude of Health Personnel Attitude to Computers Automation Cancer Clinical decision making Clinical Decision-Making - ethics Clinical Decision-Making - methods Computer Security - legislation & jurisprudence Decision making Diabetic retinopathy Diagnosis, Computer-Assisted - ethics Diagnosis, Computer-Assisted - legislation & jurisprudence Ethics, Medical Health Knowledge, Attitudes, Practice Humans Learning algorithms Machine learning Machine Learning - ethics Machine Learning - legislation & jurisprudence Medical diagnosis Medical prognosis Oncology Physicians Pneumonia Therapeutic applications Therapy, Computer-Assisted - ethics Therapy, Computer-Assisted - legislation & jurisprudence Tumors United States > US
Online Access	Get full text
ISSN	0959-8138 1756-1833 1756-1833
DOI	10.1136/bmj.l886

More Information
Summary:	To maximise the clinical benefits of machine learning algorithms, we need to rethink our approach to explanation, argue David Watson and colleagues
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0959-8138 1756-1833 1756-1833
DOI:	10.1136/bmj.l886