Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

Journal: JAMA network open
Published Date: Mar 1, 2021

Abstract

IMPORTANCE: An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer).

Authors

  • Baptiste Vasey
    Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
  • Stephan Ursprung
    Department of Radiology and Cancer Research UK Cambridge Centre, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, England.
  • Benjamin Beddoe
    Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Elliott H Taylor
    Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
  • Neale Marlow
    Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
  • Nicole Bilbro
    Department of Surgery, Maimonides Medical Center, Brooklyn, New York.
  • Peter Watkinson
    Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Kadoorie Centre for Critical Care Research and Education, Oxford OX3 9DU, UK. Electronic address: peter.watkinson@ndcn.ox.ac.uk.
  • Peter McCulloch
    Nuffield Department of Surgical Science Level 6, John Radcliffe Hospital, Oxford OX3 9DU, UK. Electronic address: peter.mcculloch@nds.ox.ac.uk.

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