Time to reality check the promises of machine learning-powered precision medicine.
Journal
The Lancet. Digital health
ISSN: 2589-7500
Titre abrégé: Lancet Digit Health
Pays: England
ID NLM: 101751302
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
24
06
2020
revised:
29
07
2020
accepted:
07
08
2020
entrez:
17
12
2020
pubmed:
18
12
2020
medline:
28
1
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33328030
pii: S2589-7500(20)30200-4
doi: 10.1016/S2589-7500(20)30200-4
pmc: PMC9060421
mid: NIHMS1702565
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e677-e680Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : K01 HL141771
Pays : United States
Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.
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