Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review.

Journal: Clinical orthopaedics and related research
Published Date: Dec 1, 2020

Abstract

BACKGROUND: Machine learning (ML) is a subdomain of artificial intelligence that enables computers to abstract patterns from data without explicit programming. A myriad of impactful ML applications already exists in orthopaedics ranging from predicting infections after surgery to diagnostic imaging. However, no systematic reviews that we know of have compared, in particular, the performance of ML models with that of clinicians in musculoskeletal imaging to provide an up-to-date summary regarding the extent of applying ML to imaging diagnoses. By doing so, this review delves into where current ML developments stand in aiding orthopaedists in assessing musculoskeletal images.

Authors

  • Olivier Q Groot
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA.
  • Michiel E R Bongers
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA.
  • Paul T Ogink
  • Joeky T Senders
    Department of Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands; Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Jos A M Bramer
    Department of Orthopedic Surgery, Academic University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
  • Jorrit-Jan Verlaan
    P. T. Ogink, J.-J. Verlaan, Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, the Netherlands.
  • Joseph H Schwab
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: jhschwab@mgh.harvard.edu.

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