How AI May Transform Musculoskeletal Imaging.

Détails

ID Serval
serval:BIB_9F452EB31F5C
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
How AI May Transform Musculoskeletal Imaging.
Périodique
Radiology
Auteur⸱e⸱s
Guermazi A., Omoumi P., Tordjman M., Fritz J., Kijowski R., Regnard N.E., Carrino J., Kahn C.E., Knoll F., Rueckert D., Roemer F.W., Hayashi D.
ISSN
1527-1315 (Electronic)
ISSN-L
0033-8419
Statut éditorial
Publié
Date de publication
01/2024
Peer-reviewed
Oui
Volume
310
Numéro
1
Pages
e230764
Langue
anglais
Notes
Publication types: Journal Article ; Review
Publication Status: ppublish
Résumé
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.
Mots-clé
Humans, Artificial Intelligence, Radionuclide Imaging, Commerce, Physical Examination, Radiologists
Pubmed
Web of science
Création de la notice
10/01/2024 10:54
Dernière modification de la notice
26/07/2024 6:01
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