Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications

Abstract

This narrative review evaluates the role of artificial intelligence (AI) algorithms in orthopaedic surgery and distinguishes itself by explaining how the main algorithmic approaches function and illustrating each with orthopaedic examples. Machine learning methods, including regression, classification and reinforcement learning, have been applied to fracture detection, prediction of revision risk and modelling of outcomes after arthroplasty and sports injury. Deep learning and convolutional neural networks have improved fracture classification, implant surveillance and segmentation of cartilage and meniscal tissue on magnetic resonance imaging. Neural networks such as FracNet and YOLO-based systems demonstrate growing capability in trauma imaging. Natural language processing has automated the extraction of operative and registry data, while large language models are emerging for diagnostic support and education. Generative artificial intelligence (GAI) have produced synthetic musculoskeletal images to expand data sets. Computer vision and image processing underpin robotic-assisted surgery and preoperative planning, and federated learning enables multicentre collaboration while protecting privacy. Each algorithm offers strengths in accuracy, efficiency or scalability, but also carries bias, transparency, computational cost and lack of external validation. This review explores how these algorithms are shaping orthopaedics, highlighting their benefits, limitations and challenges. Rigorous validation, transparent reporting and governance are essential for safe clinical use. Level of Evidence: N/A.