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

Journal: The Lancet. Digital health
Published Date: Sep 16, 2020

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.

Authors

  • Jack Wilkinson
    Centre for Biostatistics, Manchester Academic Health Science Centre, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK. Electronic address: jack.wilkinson@manchester.ac.uk.
  • Kellyn F Arnold
    Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Faculty of Medicine and Health, University of Leeds, Leeds, UK.
  • Eleanor J Murray
    Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
  • Maarten van Smeden
    Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
  • Kareem Carr
    Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
  • Rachel Sippy
    Institute for Global Health and Translational Science, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Geography, University of Florida, Gainesville, FL, USA; Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
  • Marc de Kamps
    Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; School of Computing, University of Leeds, Leeds, UK.
  • Andrew Beam
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States of America; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America.
  • Stefan Konigorski
    Digital Health & Machine Learning Research Group, Hasso Plattner Institut for Digital Engineering, Potsdam, Germany; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Christoph Lippert
  • Mark S Gilthorpe
    Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Faculty of Medicine and Health, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK.
  • Peter W G Tennant
    Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; Faculty of Medicine and Health, University of Leeds, Leeds, UK; Alan Turing Institute, London, UK.

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