Short commentary

Biomechanics has undergone a profound transformation in recent years, primarily driven by advances in computational modeling and Artificial İntelligence (AI). Traditional biomechanical analyses, which relied heavily on empirical testing and deterministic numerical methods such as the Finite Element Method (FEM), are increasingly being complemented—or even replaced—by data-driven models capable of capturing complex biological behavior with enhanced efficiency and accuracy [1,2]. This convergence of AI and numerical analysis has opened new avenues for simulating, predicting, and optimizing biological systems across multiple scales, from cellular microstructures to whole-organ mechanics [3].

In conventional biomechanics, the finite element method has long been recognized as a powerful tool for understanding tissue-level mechanical responses under various physiological and pathological conditions [4]. However, biological tissues’ nonlinear, heterogeneous, and adaptive nature poses challenges to purely physics-based models, often requiring extensive computational resources and detailed material characterization [5]. To address these limitations, researchers have increasingly integrated Machine Learning (ML) and Deep Learning (DL) techniques with FEM frameworks to build hybrid models that retain the interpretability of physics-based approaches while leveraging the predictive capacity of AI [6,7].

One of the most promising paradigms in this direction is data-driven modeling, which replaces or augments conventional constitutive equations with AI-based surrogate models trained on simulation or experimental datasets [8]. Such models can approximate stress–strain relationships, predict fracture initiation, or emulate tissue remodeling processes without explicitly solving governing equations at every iteration. For example, Hambli [2] proposed a multiscale modeling framework coupling finite element simulations with neural networks to simulate bone adaptation, significantly reducing computational time while maintaining high predictive fidelity. Similarly, Karniadakis et al. [9] introduced Physics-Informed Neural Networks (PINNs), which incorporate physical laws into deep learning architectures, ensuring that AI predictions remain consistent with fundamental biomechanical principles. Mutu [10] investigated the mechanical effects of different implant designs and biomaterials used in the treatment of oblique tibial fractures using Finite Element Analysis (FEA) and ML methods. Static structural simulations were performed in Ansys Workbench for seven implant configurations made of Ti–6Al–4V alloy and 316L stainless steel under axial loads ranging from 600 to 1000 N. From these analyses, a dataset of 1008 points containing maximum stress and total displacement values was generated and used to train three ML algorithms—Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Decision Tree (DT)—in WEKA. The results showed that implants made of 316L stainless steel exhibited higher maximum stress but lower total displacement compared to Ti–6Al–4V implants, indicating better fixation stability. Among the ML models, SVM achieved the best predictive performance, with mean absolute errors of 0.24-0.41 for maximum stress and 0.0003-0.0015 for total displacement, outperforming MLP and DT. Overall, the study demonstrated that integrating artificial intelligence with numerical analysis enables accurate, low-cost, and efficient prediction of biomechanical responses, offering valuable insights for optimizing implant design and supporting data-driven decision-making in orthopedic biomechanics.

The integration of AI with numerical analysis also enhances personalization in biomechanical modeling. By utilizing patientspecific imaging data (e.g., CT or MRI), AI algorithms can automatically extract anatomical features, assign material properties, and optimize boundary conditions, leading to more accurate predictions of stress distributions, implant performance, and fracture risk [11,12]. This personalized, data-driven modeling approach is particularly valuable in orthopedics and cardiovascular biomechanics, where individualized assessments can significantly improve clinical outcomes.

Despite its promise, data-driven biomechanics faces ongoing challenges, including the need for large, high-quality datasets, explainable AI models, and robust validation frameworks [13]. The synergy between artificial intelligence and numerical analysis marks a paradigm shift toward more adaptive, predictive, and intelligent biomechanical simulations. As the boundary between physics-based and data-driven modeling continues to blur, this hybrid approach is poised to become the cornerstone of next-generation biomechanical research and clinical applications.

Conclusion

Machine learning algorithms have emerged as powerful tools for interpreting large and complex datasets, offering highaccuracy modeling and predictive capabilities that can transform biomechanical research. When integrated with FEA, these data-driven approaches enable comprehensive evaluation of structural behavior, material properties, and loading conditions in biological systems. Numerous studies have shown that factors such as implant geometry, biomaterial selection, and mesh characteristics play a critical role in determining stress distribution, strain localization, and overall deformation behavior in fracture fixation and other orthopedic applications. By coupling FEA with intelligent algorithms such as Support Vector SVM, Artificial Neural Networks (ANN), and DT, researchers can rapidly predict biomechanical responses without the need for extensive and costly experimental testing. This synergy not only accelerates the optimization of implant design and surgical strategies but also enhances the reliability and reproducibility of computational analyses. In the future, the integration of artificial intelligence, data science, and numerical modeling is expected to enable the creation of adaptive, personalized, and cost-efficient biomechanical solutions, supporting the evolution of precision medicine and intelligent clinical decision-making systems.

References

1. Fernandez J, Hunter P, Mithraratne K. Biomechanical modeling for clinical decision making: Integrating data-driven and physicsbased models. Biomech Model Mechanobiol. 2020; 19: 1241–1255.
2. Hambli R. Multiscale computational approach for bone remodeling simulation using coupled finite element and neural network computation. Biomech Model Mechanobiol. 2011; 10: 133–145.
3. Viceconti M, Hunter P, Hose D. Big data, big knowledge: Big data for personalized healthcare. Comput Methods Programs Biomed. 2021; 200: 105898.
4. Zienkiewicz OC, Taylor RL, Zhu JZ. The finite element method: Its basis and fundamentals. 7th ed. Elsevier; 2013.
5. Yang L, Zhang J, Guo X. Multiscale modeling and simulation of bone mechanics: From microstructure to function. Acta Biomater. 2022; 146: 15–32.
6. Linka K, Hillgärtner M, Abdolazizi K, Aydin RC, Itskov M, Cyron CJ. Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning. J Comput Phys. 2020; 429: 110010.
7. Lee J, Kim D. Hybrid finite element–deep learning framework for nonlinear soft tissue mechanics. Comput Methods Biomech Biomed Eng. 2023; 26: 457–472.
8. Bessa MA, Bostanabad R, Liu Z, Hu A, Apley DW, Brinson C, et al. A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality. Comput Methods Appl Mech Eng. 2017; 320: 633–667.
9. Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021; 3: 422–440.
10. Mutu HB. A collaborative approach of finite element method and machine learning algorithms for biomechanical analysis of implants used in tibial shaft fractures. BMC Musculoskelet Disord. 2025; 26: 919.
11. Liu Y, Zhang Z, Chen Y. Deep learning-based surrogate models for patient-specific biomechanical simulations. Med Eng Phys. 2020; 83: 1–11.
12. Shah SM, Dall’Ara E, Viceconti M. Machine learning in musculoskeletal biomechanics: A review of methods and applications. Comput Methods Biomech Biomed Eng. 2022; 25: 281–298.
13. Rackwitz L, Götz T, Wriggers P. Data-driven computational mechanics: Progress, challenges, and future directions. Comput Mech. 2022; 69: 401–420.