Time to reality check the promises of machine learning-powered precision medicine
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.
Citation
Wilkinson , J , Arnold , K F , Murray , E J , van Smeden , M , Carr , K , Sippy , R , de Kamps , M , Beam , A , Konigorski , S , Lippert , C , Gilthorpe , M S & Tennant , P W G 2020 , ' Time to reality check the promises of machine learning-powered precision medicine ' , The Lancet Digital Health , vol. 2 , no. 12 , pp. e677-e680 . https://doi.org/10.1016/S2589-7500(20)30200-4
Publication
The Lancet Digital Health
Status
Peer reviewed
ISSN
2589-7500Type
Journal item
Rights
Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Description
JW is supported by a Wellcome Institutional Strategic Support Fund award (204796/Z/16/Z). MSG and PWGT are supported by The Alan Turing Institute (EP/N510129/1). CL is supported by the German Federal Ministry of Education and Research in the project KI-LAB-ITSE (project number 01|S19066).Collections
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