Advances in biomechanical modeling of lumbar spine diseases and tumors: gaps, opportunities, and AI integration.

Lumbar spine disorders encompass degenerative, deformity, osteoporotic, and neoplastic conditions that alter spinal biomechanics and contribute to pain, instability, and functional impairment. Computational modeling, particularly finite element analysis (FEA), enables estimation of internal stress distributions and load transmission patterns that cannot be directly measured in vivo. Although traditional FEA has provided important mechanistic insight, clinical applicability has been limited by labor-intensive model generation, simplified loading assumptions, and reliance on generalized anatomical properties. Recent advances in artificial intelligence (AI) and musculoskeletal modeling have expanded the scalability and physiological realism of computational spine simulation. Automated image segmentation allows efficient generation of patient-specific geometries, while physics-informed neural networks, multibody dynamics, and coupled multibody-finite element frameworks integrate data-driven learning with biomechanical principles to represent functional loading conditions. These approaches support applications including deformity correction planning, fracture risk assessment, implant optimization, and prediction of postoperative mechanical complications. Despite substantial methodological progress, routine clinical translation remains limited. Key challenges include insufficient outcome-based validation, lack of standardized reporting frameworks, and uncertainty regarding generalizability across heterogeneous patient populations. This narrative review synthesizes contemporary computational modeling strategies applied to lumbar spine disease and evaluates their clinical applications, validation requirements, and translational barriers. Emphasis is placed on the evolution of spinal simulation from investigational biomechanics toward patient-specific decision support, while highlighting the validation and outcome-based evidence required for responsible clinical integration.