Artificial Intelligence in Rotator Cuff Injury Diagnosis: A Systematic Review and Meta-Analysis

Rotator cuff tears are a leading cause of shoulder pain in adults and have a substantial impact on function and quality of life. Magnetic resonance imaging and ultrasonography are widely used for diagnosis, but interpretation can be time-consuming and operator-dependent. Recent advances in artificial intelligence, particularly deep learning, have demonstrated promising performance in musculoskeletal imaging. However, evidence regarding the diagnostic accuracy and clinical outcome prediction of AI-based models for rotator cuff tears remains fragmented. This systematic review and meta-analysis aim to synthesize available evidence and provide pooled estimates of diagnostic performance.