YOLO11m-Based Artificial Intelligence Versus Orthopaedic Physician Panels for Arthroscopy-Referenced Three-Category MRI Classification of the Posterosuperior Rotator Cuff: A Multi-Reader Diagnostic Accuracy Study

Background/Objectives: Artificial intelligence (AI) may support rotator cuff MRI interpretation; however, its performance relative to clinicians at different experience levels remains uncertain. We compared a locked YOLO11m-based model with prespecified orthopaedic physician panels using arthroscopy as the reference standard. Methods: This retrospective single-centre study included a separate 300-patient model-development dataset and an arthroscopy-referenced reader cohort of 258 patients (60 intact cuffs, 86 partial-thickness tears, and 112 full-thickness tears). Twelve blinded physicians were assigned to four three-reader experience panels, and panel diagnoses were defined by a two-of-three majority. Paired accuracy comparisons used exact McNemar tests with Holm adjustment. Results: AI achieved 0.841 overall accuracy (95% CI, 0.792–0.881). The senior-specialist panel had numerically higher accuracy (0.911), but the difference from AI was not statistically significant after adjustment (Holm p = 0.060); the junior-specialist panel also did not differ from AI (0.822; Holm p = 0.649). AI showed higher paired accuracy than the senior-resident (0.729; Holm p = 0.005) and junior-resident panels (0.671; Holm p < 0.001). Partial-thickness tears had the lowest AI sensitivity (0.709). Conclusions: In this restricted-cine, three-category benchmark, AI outperformed both resident panels, whereas neither specialist panel comparison reached the adjusted significance threshold. These findings do not establish equivalence to specialists or benefit from AI-assisted reading.