How AI May Transform Musculoskeletal Imaging

Ali Guermazi, Patrick Omoumi, Mickael Tordjman, Jan Fritz, Richard Kijowski, Nor Eddine Regnard, John Carrino, Charles E. Kahn, Florian Knoll, Daniel Rueckert, Frank W. Roemer, Daichi Hayashi

Publikation: Beitrag in FachzeitschriftÜbersichtsartikelBegutachtung

2 Zitate (Scopus)

Abstract

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist’s workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

OriginalspracheEnglisch
Aufsatznummere230764
FachzeitschriftRadiology
Jahrgang310
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - Jan. 2024

Fingerprint

Untersuchen Sie die Forschungsthemen von „How AI May Transform Musculoskeletal Imaging“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren