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Despite standard rehabilitation protocols, many patients still suffer from limited mobility, delayed union, or even non-union. This underscores the need for personalized rehabilitation protocols. Fracture healing is a dynamic process governed by the interplay of mechanical stimuli and biochemical signalling pathways. This review first summarizes current understanding of the biological and mechanobiological mechanisms that regulate bone repair. It also discusses different simulation models, including the finite element method (FEM), agent-based models (ABM), reaction–diffusion models (RDM), and machine learning (ML), and evaluates their respective strengths. Recent advances in patient-specific models are also reviewed, particularly those integrating CT-derived geometry, bone properties, and musculoskeletal (MSK) loading. These approaches enable individualized predictions of healing and can inform clinical rehabilitation strategies. Finally, the key challenges and future priorities for implementing these technologies in clinical practice are discussed, providing insights to support the development of more precise and patient-specific fracture care. • This review outlines key mechanobiological theories of fracture healing. • It summarizes major modelling approaches including FEM, ABM, RDM, and ML. • Patient-specific models incorporating CT geometry and MSK loading are reviewed. • Machine learning is highlighted as a tool for real-time rehabilitation guidance. • A digital twin framework is proposed for personalized fracture management.
Li et al. (Wed,) studied this question.