Purpose: Musculoskeletal (MSK) radiography is one of the highest-volume imaging services in contemporary radiology practice. Despite its apparent simplicity, interpretation requires sustained vigilance and is frequently performed by trainees or general radiologists, introducing duplicated cognitive effort and operational inefficiencies. This review evaluates artificial intelligence (AI) as a structural adjunct to improve diagnostic accuracy, workflow efficiency, and long-term sustainability in high-volume MSK radiography.
Methods: A narrative review of contemporary literature was conducted, focusing on AI applications in fracture detection, triage prioritization, concurrent interpretive support, workflow integration, and governance considerations.
Results: Deep learning algorithms consistently demonstrate sensitivities exceeding 90% for fracture detection. AI assistance narrows performance gaps between trainees and subspecialists, reduces inter-reader variability, and enables dynamic worklist prioritization. Beyond diagnostic accuracy, AI may mitigate cumulative cognitive load and stabilize performance during high-volume interpretation. Limitations include false positives, automation bias, dataset shift, and variability in external validation.
Conclusion: When deployed as an adjunct rather than a replacement, AI represents a pragmatic and potentially structural strategy for enhancing efficiency, diagnostic consistency, and sustainability in high-volume MSK radiography.