This chapter provides a comprehensive examination of artificial intelligence (AI) applications in orthopedic medicine, analyzing the transformation from traditional diagnostic approaches to data-driven precision medicine across the musculoskeletal care continuum. The integration of AI technologies has fundamentally reshaped fracture detection, surgical interventions, outcome prediction, rehabilitation monitoring, and biomechanics modeling in orthopedic practice. Contemporary AI systems demonstrate diagnostic performance matching or exceeding specialist expertise, achieving pooled sensitivity and specificity rates consistently above 90% across multiple anatomical locations and imaging modalities. The exponential growth in research output, with 52.5% of orthopedic AI studies published in 2024 alone, reflects the rapid acceleration of clinical applications. Deep learning approaches (43.3%) dominate current implementations, with fracture detection (24.4%) and outcome prediction (21.2%) representing primary use cases. Clinical applications demonstrate measurable impact: AI-assisted fracture detection reduces missed diagnoses by 15–25% and interpretation times by 30–40% in emergency departments. Robotic systems with AI integration achieve sub-millimeter surgical precision in joint arthroplasty, significantly improving component alignment compared to conventional techniques. Predictive analytics consistently outperform traditional scoring systems, with area under the curve improvements of 0.10–0.15 for complication prediction. Specialized applications extend to diabetic foot ulcer management, achieving 81–97% accuracy in diagnostic imaging analysis. Despite remarkable technical achievements, significant implementation challenges persist. Only 14.5% of studies undergo external validation, with merely 3.2% reporting prospective clinical validation, highlighting substantial gaps between research and clinical translation. Data heterogeneity, algorithmic bias, and regulatory framework limitations represent major barriers to widespread adoption. Emerging technologies including digital twins, generative AI, federated learning, and edge computing promise to address current limitations while expanding AI’s transformative potential. The convergence of these technologies with multimodal data integration will enable unprecedented personalization in orthopedic care, fundamentally transforming musculoskeletal medicine delivery and patient outcomes.