Artificial intelligence in the interpretation of upper extremity trauma radiographs: a systematic review and meta-analysis.

Journal: JSES reviews, reports, and techniques
Published Date: Mar 8, 2025

Abstract

BACKGROUND: Upper extremity fractures represent a significant reason for emergency room visits; however, nonexpert readings commonly lead to diagnostic errors, particularly missed fractures. Artificial intelligence (AI) has emerged as a promising tool to aid in fracture detection, but it has been shown to be comparable to physicians at best, so it remains unclear whether there is value in its increasing implementation. This review aims to analyze the existing literature on AI in the identification and interpretation of upper extremity fractures on x-ray and to assess the diagnostic performance of such AI models.

Authors

  • Matthew Mellon
    Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
  • Joshua Dworsky-Fried
    Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
  • Preksha Rathod
    Division of Orthopedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
  • Darshil Shah
    University of Kentucky, Lexington, KY.
  • Moin Khan
    Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada; and.
  • James Yan
    Division of Orthopedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.

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