Artificial Intelligence in Orthopaedic Surgery: Clinical Applications, Socioeconomic Implications, and Future Directions — A Narrative Review

Background: Artificial intelligence (AI) is transforming orthopaedic medicine, offering new possibilities for improving diagnostic accuracy, optimizing preoperative planning, and enhancing surgical precision. Despite growing evidence of algorithmic performance, clinical translation remains uneven, and a comprehensive synthesis of current evidence is needed to guide orthopaedic practice. This review also evaluates the socioeconomic implications of AI adoption, including clinical trust, accessibility, and the transformation of healthcare delivery models. Methods: This narrative review was conducted in March and April 2026 using PubMed/MEDLINE, Scopus, and Google Scholar. Publications in English from January 2018 to April 2026 were eligible. Methodological quality was assessed using the SANRA scale. A total of 48 sources were included. Results: Deep learning models achieve pooled sensitivity and specificity exceeding 90% in fracture detection on plain radiographs, with regulatory recognition by NICE confirming clinical readiness. AI demonstrates high accuracy in osteoarthritis grading and reduces morphometric measurement times by up to 87%. Deep learning MRI reconstruction reduces acquisition times by up to 90% while maintaining diagnostic quality. AI-driven 3D preoperative templating improves acetabular cup conformity from 72.2% to 90.9% over conventional 2D methods. Robotic-assisted total knee arthroplasty achieves significantly lower mechanical alignment outlier rates than conventional surgery, though short-term patient-reported outcomes remain comparable. Emerging applications include smart sensor-embedded implants, digital twin technology, and large language models supporting clinical documentation. Conclusions: AI demonstrates evidence-supported utility across orthopaedic diagnostics, surgical planning, and arthroplasty. Significant challenges persist regarding external validation, algorithmic transparency, regulatory harmonization, and equitable access. Future research should prioritize prospective multicenter trials, explainable AI frameworks, federated learning, and health economic analyses across diverse healthcare systems.