Digital twins and multimodal artificial intelligence in spine care: a scoping review of concepts, evidence, and translational barriers

PURPOSE: This scoping review examines current evidence supporting multimodal artificial intelligence, continuous monitoring, and digital twin concepts in spine care. Our primary aims were to (1) characterize the state of digital twin development in spine care, (2) identify key technological and conceptual gaps, and (3) evaluate translational barriers to clinical implementation. METHODS: A scoping review was conducted following PRISMA-ScR guidelines. PubMed/MEDLINE, Scopus, and Web of Science were searched for studies published between January 2010 and March 2025. Findings were synthesized qualitatively. RESULTS: Twenty-six studies met inclusion criteria. Existing spine prediction models demonstrate modest discrimination and are predominantly static. Imaging-based AI shows weak associations with pain and disability. Wearable sensor monitoring is feasible but lacks consistent evidence for improved outcomes. Spine-specific digital twins remain conceptual, with no prospective validation demonstrating improved decision-making. CONCLUSION: Multimodal AI-enabled digital twins represent a compelling conceptual framework for personalized spine care, but current evidence does not support clinical superiority or readiness for implementation. Progress will require prospective validation, standardized data integration, and regulatory clarity.