TY - JOUR
T1 - Time to reality check the promises of machine learning-powered precision medicine
AU - Wilkinson, Jack
AU - Arnold, Kellyn F.
AU - Murray, Eleanor J.
AU - van Smeden, Maarten
AU - Carr, Kareem
AU - Sippy, Rachel
AU - de Kamps, Marc
AU - Beam, Andrew
AU - Konigorski, Stefan
AU - Lippert, Christoph
AU - Gilthorpe, Mark S.
AU - Tennant, Peter W.G.
N1 - Funding Information:
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). These funders had no role in any aspect of the conception or realisation of the manuscript. JW had final responsibility for the decision to submit for publication.
Publisher Copyright:
© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85096916889&partnerID=8YFLogxK
U2 - 10.1016/S2589-7500(20)30200-4
DO - 10.1016/S2589-7500(20)30200-4
M3 - Review article
C2 - 33328030
AN - SCOPUS:85096916889
VL - 2
SP - e677-e680
JO - The Lancet Digital Health
JF - The Lancet Digital Health
IS - 12
ER -