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

Saved in:

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
ISSN:	0959-8138, 1756-1833, 1756-1833
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
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