Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models
Artikel i vetenskaplig tidskrift, 2025
Considering increasing imaging volumes and a relative shortage of subspecialized musculoskeletal radiologists, AI offers the potential for a scalable solution, democratising accurate diagnosis and therefore quality of care. By triaging imaging examinations and assisting with image interpretation, AI-based tools furthermore reduce reporting times. The integration of AI into clinical practice is not intended to replace radiologists but to augment their capabilities, allowing them to manage increasing workloads effectively [12].
While AI has demonstrated significant potential in musculoskeletal imaging, its clinical adoption requires validation, ethical oversight, and consideration of its limitations. This review aims to explore these aspects to provide a balanced perspective on the future of AI in musculoskeletal radiology including exploration of the types of AI models utilised, ethical considerations, technical challenges, and current solutions.
deep learning
AI in musculoskeletal imaging
clinical integration
image analysis
computer vision
Författare
Felix C. Oettl
Universität Zürich
Hosp Special Surg
Balint Zsidai
Göteborgs universitet
Jacob F. Oeding
Göteborgs universitet
Michael T. Hirschmann
Universität Basel
Kantonsspital Baselland
Robert Feldt
Chalmers, Data- och informationsteknik, Software Engineering
David Fendrich
Tenfifty AB
Matthew J. Kraeutler
Göteborgs universitet
Philipp W. Winkler
Johannes Kepler Univ Linz
Pawel Szaro
Göteborgs universitet
Kristian Samuelsson
Göteborgs universitet
Knee Surgery, Sports Traumatology, Arthroscopy
0942-2056 (ISSN) 1433-7347 (eISSN)
Vol. In PressÄmneskategorier (SSIF 2025)
Radiologi och bildbehandling
DOI
10.1002/ksa.12702
PubMed
40450562