Background: Orthopedic implants have revolutionized the management of degenerative, traumatic, and congenital musculoskeletal conditions. However, implant failure due to loosening, wear, and mechanical mismatch with host bone remains a persistent clinical challenge. Biomechanical modeling—encompassing finite element analysis (FEA), multibody dynamics (MBD), and computational fluid dynamics (CFD)—has emerged as a critical tool for pre-clinical evaluation of implants. By simulating bone–implant interactions under physiological loads, these computational methods enable prediction of stress distribution, micromotion, wear, and fatigue. Despite these advances, their integration into routine clinical decision-making is still evolving, and questions remain about their real-world predictive accuracy. Objectives: This systematic review aims to: (1) identify and classify the range of biomechanical modeling techniques applied in orthopedic implant design; (2) analyze their role in predicting implant performance, durability, and failure mechanisms; (3) evaluate validation strategies against experimental or clinical benchmarks; and (4) assess the translational relevance of simulation outcomes for clinical practice and regulatory approval. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, and Cochrane Library up to August 2025. Studies were included if they reported the use of computational modeling techniques in orthopedic implants (hip, knee, spine, trauma fixation, or upper limb prostheses) and provided experimental or clinical validation data. Two reviewers independently performed screening, data extraction, and risk-of-bias assessment. Extracted data included modeling methodology, boundary conditions, material assumptions, validation approaches, clinical endpoints, and reported translational impact. Results: Of 1,532 screened articles, 48 studies fulfilled inclusion criteria. FEA (n=35) was the most widely employed technique, applied predominantly in total hip and knee arthroplasty. MBD (n=8) was used in dynamic simulations of spinal and knee biomechanics, while CFD (n=5) was primarily applied to joint lubrication and wear analyses. Validation strategies varied: 60% compared outcomes with cadaveric or mechanical bench-top testing, 25% correlated findings with longitudinal patient data, and 15% reported no external validation. FEA models reliably predicted high-risk zones for implant loosening, stress shielding, and periprosthetic fracture, while MBD improved dynamic gait simulation fidelity. However, inter-study heterogeneity in material properties, meshing strategies, and loading conditions limited comparability. Importantly, only a minority of studies demonstrated direct influence on implant regulatory approval or clinical guideline development. Conclusions: Biomechanical modeling has become indispensable in pre-clinical orthopedic implant design, offering powerful insights into stress distribution, wear prediction, and failure mechanisms. Nevertheless, translation to clinical relevance remains constrained by methodological variability, limited long-term validation, and underrepresentation of patient-specific models. To maximize impact, future research should prioritize standardized protocols, integration of imaging-derived patient-specific geometries, multi-scale modeling of bone remodeling, and prospective clinical validation. Establishing regulatory frameworks for simulation-driven design will be pivotal in bridging the gap between computational innovation and improved patient outcomes.
Tony A. Jubran (2025) studied this question.