Artificial intelligence empowering the diagnosis and treatment of rotator cuff tears: A systematic review from image optimization to prognosis prediction

Artificial intelligence (AI) is being developed for image acquisition, quantitative tissue assessment, diagnosis, and clinical prediction in rotator cuff tears. The breadth of this literature and its methodological limitations have not been integrated across the complete care pathway.

We searched PubMed, Embase, Web of Science, Scopus, and the Cochrane Library from inception to July 5, 2026 for English-language original studies applying AI to rotator cuff-related reconstruction, segmentation or quantitative imaging, diagnosis or classification, anatomical measurement, or prognosis. Two reviewers screened reports and extracted study, data, model, validation, reference standard, and performance characteristics. Diagnostic accuracy studies were assessed with QUADAS-2 and prediction-model studies with PROBAST. Because clinical tasks, modalities, models, reference standards, and reported metrics were not sufficiently comparable, evidence was synthesized narratively rather than pooled statistically.