Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data

Orthopedic regenerative medicine increasingly calls for integrative approaches that can capture the molecular, structural, and clinical complexity of musculoskeletal disorders. This review examines the emerging role of AI-driven multimodal data integration in orthopedic regenerative medicine, focusing on how imaging, omics, and clinical data can be synergistically combined to improve disease characterization and personalized therapeutic design. Single-modality approaches often fail to explain the heterogeneity of disorders such as osteoporosis, osteoarthritis (OA), critical-sized bone defects, and intervertebral disc degeneration (IVDD). By synthesizing recent advances in data landscapes, fusion strategies, and AI frameworks, this review highlights how integrated models connect tissue-level phenotypes with molecular mechanisms. In osteoporosis, multimodal fusion demonstrates superior fracture risk prediction; for OA, MRI-clinical integration improves diagnostic accuracy; in IVDD, multi-omics integration identifies novel biomarkers for early degeneration detection. The importance of this field lies in its potential to advance AI-driven multimodal integration for orthopedic regeneration through earlier diagnosis, more accurate prognosis, biomarker discovery, and the rational development of individualized regenerative interventions, including scaffold design and treatment selection. At the same time, the literature points to persistent challenges, including data heterogeneity, limited interpretability, incompletely paired datasets, and insufficient external validation. Overall, AI-driven multimodal data integration offers a promising foundation for next-generation regenerative orthopedics. Further progress will depend on standardized multicenter infrastructures, explainable integration models, and prospective clinical validation to support robust and clinically actionable implementation.