AI in Musculoskeletal Imaging: An End-to-End Perspective.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy.
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università Degli Studi di Milano, Via Della Commenda 10, 20122 Milan, Italy.
- Dipartimento di Scienze Biomediche per la Salute, Università Degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy.
- IRCCS Ospedale Galeazzi-Sant'Ambrogio, Via Cristina Belgioioso 173, 20157 Milan, Italy.
- U.O.C. Radiodiagnostica, ASST Centro Specialistico Ortopedico Traumatologico Gaetano Pini-CTO, P.zza Cardinal Ferrari 1, 20122 Milan, Italy.
- Department of Life Sciences, Health and Health Professions, Link Campus University, 00165 Rome, Italy.
- Department of Oncology and Hemato-Oncology, Università Degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy.
Abstract
Artificial intelligence (AI) is increasingly reshaping musculoskeletal (MSK) imaging across the entire imaging pathway. This narrative review summarizes current AI applications in MSK radiology across four domains: acquisition and reconstruction, detection and triage, characterization and quantification, and prognosis and decision support. AI-based reconstruction has enabled faster MRI acquisitions, improved denoising and artifact reduction, and supported low-dose CT imaging while preserving diagnostic quality. Fracture detection and triage currently represent the most mature clinical applications, particularly in emergency settings. AI is also promoting a shift from qualitative interpretation to quantitative imaging phenotyping through automated assessment of body composition, cartilage, bone density, degenerative spine disease, skeletal maturity, and lesion heterogeneity. Emerging applications in prognostic modeling, implant evaluation, and multimodal risk stratification remain promising but less mature. Broader clinical implementation is still limited by restricted interpretability, dataset bias, insufficient prospective validation, regulatory complexity, and unresolved medico-legal issues. Overall, AI should be viewed as a tool to augment, not replace, radiological expertise.