Artificial intelligence (AI) is increasingly shaping orthopedic practice, including diagnostics, surgical planning, postoperative monitoring, and education. Despite its growth, many surgeons and residents remain unfamiliar with AI concepts, applications, and strategies for responsible integration, including explainability, fairness, and uncertainty-aware AI. This narrative review synthesizes current literature on AI in orthopedics, covering terminology, key technologies (machine learning, deep learning, convolutional neural networks, natural language processing, computer vision, robotics, pattern recognition, predictive analytics, explainable AI, fairness, and uncertainty-aware AI), clinical applications, and educational implications. AI enhances care across the orthopedic continuum. In diagnostics, AI-assisted imaging improves fracture detection, tumor and infection identification, and implant recognition. Preoperative planning and surgical navigation benefit from the use of automated templating, 3D planning, and robotic systems. Postoperative monitoring utilizes wearable devices, sensors, and predictive analytics to track recovery and identify potential complications. Educational applications include video-based skill assessment, VR simulation with adaptive feedback, and personalized learning analytics. Ethical and practical considerations, such as accountability, data privacy, regulatory compliance, transparency, and bias mitigation, are crucial for the safe adoption of AI. Structured AI literacy and hands-on training are recommended to prepare residents for effective integration. AI can augment decision-making, enhance surgical precision, and improve resident education. Safe and responsible adoption requires foundational AI knowledge, critical appraisal of tools, and integration as a complementary resource while retaining ultimate clinical judgment. This review provides a framework for understanding AI terminology, applications, and strategies for the responsible incorporation of AI into orthopedic practice and training.
Regmi et al. (Sun,) studied this question.