aceArtificial intelligence empowering the diagnosis and treatment of rotator cuff tears
Background: 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. Methods: 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…
Most diagnostic and all completed prediction-model studies of AI for rotator cuff tears were rated high risk of bias, so the evidence cannot support routine clinical use.
The evidence base is limited by selection methods, imperfect reference standards, limited external validation, and small or selected datasets, and the studies were too heterogeneous to pool statistically.
Evidence
- Peer-reviewedMedicine2026-10-16
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Truvace Impact Record TRV-2026-1698, v1: “Artificial intelligence empowering the diagnosis and treatment of rotator cuff tears.” Truvace, 2026-10-17. /record/TRV-2026-1698 (accessed at citation time). sha256 b92bb14d96b3aba4…
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