Artificial intelligence empowering the diagnosis and treatment of rotator cuff tears: A systematic review from image optimization to prognosis prediction.
- 1Orthopaedic Center, The Affiliated Hospital of Beihua University, Jilin, Jilin, China.
- 2Orthopaedic Center, The Second Hospital Affiliated to Jilin University, Changchun, Jilin, China.
Abstract
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 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.Results
Sixty-six reports were included: 35 diagnostic accuracy or classification studies, 20 segmentation or quantitative imaging studies, 9 prognosis or outcome prediction studies, 1 Magnetic Resonance Imaging reconstruction or image-optimization study, and 1 exploratory unsupervised-phenotyping study. Applications spanned Magnetic Resonance Imaging, radiography, ultrasound, computed tomography, clinical or multimodal data, motion video, and emerging sensing data. QUADAS-2 classified overall risk-of-bias as high in 27/36 diagnostic studies, unclear in 6/36, and low in 3/36. PROBAST classified all 8/8 completed prediction-model studies as high risk-of-bias; 1 protocol without model results was not rated. Promising internal performance was recurrent, but selection methods, imperfect or incompletely described reference standards, limited external validation, small or selected datasets, and analysis-stage weaknesses constrained clinical inference.Conclusion
AI has proof-of-concept value across rotator cuff imaging and decision support, but the current evidence does not establish routine clinical readiness or replacement of clinician judgment. Prospective multicenter studies, transparent reference standards, independent external validation, calibration and clinical-utility assessment, and standardized task-specific reporting are required.
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BibTeX
@article{wang2026artificial,
title = {{Artificial intelligence empowering the diagnosis and treatment of rotator cuff tears: A systematic review from image optimization to prognosis prediction.}},
author = {Haitao Wang and Deming Guo and Yingqi Gao and Weixin Xian and Liyan Zhang},
journal = {Medicine},
year = {2026},
month = oct,
doi = {10.1097/md.0000000000050739},
url = {https://europepmc.org/article/MED/42888543},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Artificial intelligence empowering the diagnosis and treatment of rotator cuff tears: A systematic review from image optimization to prognosis prediction.
AU - Haitao Wang
AU - Deming Guo
AU - Yingqi Gao
AU - Weixin Xian
AU - Liyan Zhang
PY - 2026
DA - 2026/10/01
JO - Medicine
DO - 10.1097/md.0000000000050739
UR - https://europepmc.org/article/MED/42888543
AB - 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 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.Results Sixty-six reports were included: 35 diagnostic accuracy or classification studies, 20 segmentation or quantitative imaging studies, 9 prognosis or outcome prediction studies, 1 Magnetic Resonance Imaging reconstruction or image-optimization study, and 1 exploratory unsupervised-phenotyping study. Applications spanned Magnetic Resonance Imaging, radiography, ultrasound, computed tomography, clinical or multimodal data, motion video, and emerging sensing data. QUADAS-2 classified overall risk-of-bias as high in 27/36 diagnostic studies, unclear in 6/36, and low in 3/36. PROBAST classified all 8/8 completed prediction-model studies as high risk-of-bias; 1 protocol without model results was not rated. Promising internal performance was recurrent, but selection methods, imperfect or incompletely described reference standards, limited external validation, small or selected datasets, and analysis-stage weaknesses constrained clinical inference.Conclusion AI has proof-of-concept value across rotator cuff imaging and decision support, but the current evidence does not establish routine clinical readiness or replacement of clinician judgment. Prospective multicenter studies, transparent reference standards, independent external validation, calibration and clinical-utility assessment, and standardized task-specific reporting are required.
SN - 0304-5412
SN - 1536-5964
ER -