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How AI May Transform Musculoskeletal Imaging.

Ali Guermazi1, Patrick Omoumi1, Mickael Tordjman1

  • 1From the Department of Radiology, Boston University School of Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.); Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu Hospital and University Paris Cité, Paris, France (M.T.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (J.F., R.K.); Gleamer, Paris, France (N.E.R.); Réseau d'Imagerie Sud Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.); Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell Medicine, New York, NY (J.C.); Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.); Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and Radiology (F.W.R.), Universitätsklinikum Erlangen & Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany (F.K.); School of Medicine & Computation, Information and Technology Klinikum rechts der Isar, Technical University Munich, München, Germany (D.R.); Department of Computing, Imperial College London, London, England (D.R.); and Department of Radiology, Tufts Medical Center, Tufts University School of Medicine, Boston, Mass (D.H.).

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Summary
This summary is machine-generated.

Artificial intelligence (AI) can address the shortage of musculoskeletal radiologists by improving workflow and reporting times. Collaboration between AI researchers and radiologists is key for trusted implementation, enhancing expert capabilities.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Increasing musculoskeletal imaging volumes outpace the availability of subspecialized musculoskeletal radiologists.
  • A critical need exists for solutions to manage growing imaging workloads efficiently.

Purpose of the Study:

  • To explore the potential of artificial intelligence (AI) as a solution to the radiologist shortage in musculoskeletal imaging.
  • To outline the requirements for successful clinical implementation of AI in radiology.

Main Methods:

  • The study discusses the integration of AI-supported data acquisition and interpretation tools into clinical practice.
  • It emphasizes the necessity of collaboration between AI researchers and clinical radiologists for validation and trust.

Main Results:

  • AI tools can enhance musculoskeletal radiologists' workflow through triaging, interpretation assistance, and reduced reporting times.
  • Successful integration of AI offers benefits in business, education, and research within musculoskeletal radiology.

Conclusions:

  • AI is poised to augment, not replace, musculoskeletal radiologists, enhancing their capabilities.
  • Trusted and reliable AI implementation through collaboration is essential for improving musculoskeletal radiology services.