Time to reality check the promises of machine learning-powered precision medicine

Jack Wilkinson, Kellyn F Arnold, Eleanor J Murray, Maarten van Smeden, Kareem Carr, Rachel Sippy, Marc de Kamps, Andrew Beam, Stefan Konigorski, Christoph Lippert, Mark S Gilthorpe, Peter W G Tennant

Research output: Contribution to journalComment/debatepeer-review

119 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)e677-e680
Number of pages4
JournalThe Lancet Digital Health
Issue number12
Early online date16 Sept 2020
Publication statusPublished - Dec 2020


  • Delivery of Health Care/methods
  • Humans
  • Machine Learning
  • Precision Medicine/methods


Dive into the research topics of 'Time to reality check the promises of machine learning-powered precision medicine'. Together they form a unique fingerprint.

Cite this