Commercially available artificial intelligence tools for fracture detection: the evidence.

Journal: BJR open
Published Date:

Abstract

Missed fractures are a costly healthcare issue, not only negatively impacting patient lives, leading to potential long-term disability and time off work, but also responsible for high medicolegal disbursements that could otherwise be used to improve other healthcare services. When fractures are overlooked in children, they are particularly concerning as opportunities for safeguarding may be missed. Assistance from artificial intelligence (AI) in interpreting medical images may offer a possible solution for improving patient care, and several commercial AI tools are now available for radiology workflow implementation. However, information regarding their development, evidence for performance and validation as well as the intended target population is not always clear, but vital when evaluating a potential AI solution for implementation. In this article, we review the range of available products utilizing AI for fracture detection (in both adults and children) and summarize the evidence, or lack thereof, behind their performance. This will allow others to make better informed decisions when deciding which product to procure for their specific clinical requirements.

Authors

  • Cato Pauling
    UCL Great Ormond Street Institute of Child Health, University College London, London WC1E 6BT, United Kingdom.
  • Baris Kanber
    Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London WC1N 3BG, United Kingdom.
  • Owen J Arthurs
    UCL Great Ormond Street Institute of Child Health, University College London, London WC1E 6BT, United Kingdom.
  • Susan C Shelmerdine
    UCL Great Ormond Street Institute of Child Health, University College London, London WC1E 6BT, United Kingdom.

Keywords

No keywords available for this article.