Wilkinson, J, Arnold, KF orcid.org/0000-0002-0911-5029, Murray, EJ et al. (9 more authors) (2020) Time to reality check the promises of machine learning-powered precision medicine. The Lancet Digital Health. ISSN 2589-7500
Abstract
Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype. We argue that the goal of personalised medical care faces serious challenges, many of which cannot be addressed through algorithmic complexity, and call for collaboration between traditional methodologists and experts in medical machine learning to avoid extensive research waste.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Nov 2020 13:55 |
Last Modified: | 03 Nov 2020 13:55 |
Status: | Published online |
Publisher: | Elsevier BV |
Identification Number: | 10.1016/s2589-7500(20)30200-4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167291 |