The AI-powered orthopedic digital twin: a new paradigm for personalized diagnosis, surgical simulation, and prognostic management - a scoping review

Musculoskeletal disorders represent a leading cause of chronic pain and long-term disability worldwide, with conventional orthopedic practices often limited by one-size-fits-all management strategies. This scoping review maps the current landscape of evidence on artificial intelligence (AI)-integrated digital twin (DT) systems in orthopedics, focusing on their conceptual frameworks, clinical applications in personalized diagnosis, surgical simulation and prognostic management, and existing translational gaps. A systematic literature search was conducted across six electronic databases (PubMed, Embase, Web of Science, IEEE Xplore, ACM Digital Library and Scopus) up to 10 February 2026. Fourteen studies published between 2021 and 2026 met the inclusion criteria, with data charted across four predefined core dimensions: patient-specific DT construction, AI-driven biomechanical modeling, diagnostic stratification and surgical simulation, and individualized prognostic modeling. Accumulating preliminary evidence indicates that AI enables orthopedic DTs to evolve from static anatomical replicas to semi-dynamic predictive frameworks, with proof-of-concept applications validated across joint arthroplasty, spinal rehabilitation, foot and ankle surgery, and trauma care. The convergence of AI and DT technologies suggests preliminary technical feasibility and promising translational potential for personalized orthopedic care. However, the field remains in an early translational phase, limited by small sample sizes, single-center designs, insufficient large-scale clinical validation, and heterogeneous methodologies. Further multicenter clinical trials and standardized construction pipelines are required to confirm the clinical effectiveness, safety, and generalizability of AI-powered DT systems in routine orthopedic practice.