Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.

Journal: BMJ (Clinical research ed.)
Published Date: Mar 25, 2020

Abstract

OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.

Authors

  • Myura Nagendran
    Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Imperial College London, UK myura.nagendran@imperial.ac.uk.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Christopher A Lovejoy
    Cera Care, London, UK. christopher.lovejoy1@gmail.com.
  • Anthony C Gordon
    Department of Surgery and Cancer, Imperial College London, London, UK. anthony.gordon@imperial.ac.uk.
  • Matthieu Komorowski
    Imperial College London, London, UK.
  • Hugh Harvey
    Institute of Cognitive Neurosciences, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London WC1N 3AZ, England.
  • Eric J Topol
    Scripps Research Translational Institute, La Jolla, CA 92037, USA; Scripps Clinic Division of Cardiovascular Diseases, La Jolla, CA 92037, USA. Electronic address: etopol@scripps.edu.
  • John P A Ioannidis
    Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California.
  • Gary S Collins
    Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Mahiben Maruthappu
    Cera Care, London.

Keywords

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