An overview of artificial intelligence and machine learning in shoulder surgery

Machine learning (ML), a subset of artificial intelligence (AI), utilizes advanced algorithms to learn patterns from data, enabling accurate predictions and decision-making without explicit programming. In orthopedic surgery, ML is transforming clinical practice, particularly in shoulder arthroplasty and rotator cuff tears (RCTs) management. This review explores the fundamental paradigms of ML, including supervised, unsupervised, and reinforcement learning, alongside key algorithms such as XGBoost, neural networks, and generative adversarial networks. In shoulder arthroplasty, ML accurately predicts postoperative outcomes, complications, and implant selection, facilitating personalized surgical planning and cost optimization. Predictive models, including ensemble learning methods, achieve over 90% accuracy in forecasting complications, while neural networks enhance surgical precision through AI-assisted navigation. In RCTs treatment, ML enhances diagnostic accuracy using deep learning models on magnetic resonance imaging and ultrasound, achieving area under the curve values exceeding 0.90. ML models also predict tear reparability with 85% accuracy and postoperative functional outcomes, including range of motion and patient-reported outcomes. Despite remarkable advancements, challenges such as data variability, model interpretability, and integration into clinical workflows persist. Future directions involve federated learning for robust model generalization and explainable AI to enhance transparency. ML continues to revolutionize orthopedic care by providing data-driven, personalized treatment strategies and optimizing surgical outcomes.