Clinical Applications of Machine Learning Algorithms: Beyond the Black Box

9 Pages Posted: 7 Apr 2019

See all articles by David Watson

David Watson

University of Oxford, Oxford Internet Institute, Students

Jenny Krutzinna

University of Bergen - Department of Administration and Organization Theory

Ian Bruce

University of Manchester

Christopher Griffiths

University of Manchester

Iain McInnes

University of Glasgow

Michael Barnes

Queen Mary University of London

Luciano Floridi

University of Oxford - Oxford Internet Institute

Date Written: March 12, 2019

Abstract

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Keywords: Machine learning, explainability, black box algorithms

Suggested Citation

Watson, David and Krutzinna, Jenny and Bruce, Ian and Griffiths, Christopher and McInnes, Iain and Barnes, Michael and Floridi, Luciano, Clinical Applications of Machine Learning Algorithms: Beyond the Black Box (March 12, 2019). Available at SSRN: https://ssrn.com/abstract=3352454 or http://dx.doi.org/10.2139/ssrn.3352454

David Watson (Contact Author)

University of Oxford, Oxford Internet Institute, Students ( email )

1 St. Giles
Oxford, Oxfordshire OX1 3JS
United Kingdom

Jenny Krutzinna

University of Bergen - Department of Administration and Organization Theory ( email )

Norway

Ian Bruce

University of Manchester ( email )

Oxford Road
Manchester, M13 9PL
United Kingdom

Christopher Griffiths

University of Manchester ( email )

Oxford Road
Manchester, M13 9PL
United Kingdom

Iain McInnes

University of Glasgow ( email )

Adam Smith Business School
Glasgow, Scotland G12 8LE
United Kingdom

Michael Barnes

Queen Mary University of London ( email )

Mile End Rd
Mile End Road
London, London E1 4NS
United Kingdom

Luciano Floridi

University of Oxford - Oxford Internet Institute ( email )

1 St. Giles
University of Oxford
Oxford OX1 3PG Oxfordshire, Oxfordshire OX1 3JS
United Kingdom

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