# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Composite Materials
developed a neural network called \"\(\beta\)Low\" to recommend new low-modulus \(\beta\) titanium alloys for biomedical applications, as shown in Figure 12. Despite limited data, the network identified a promising alloy (Ti-12 Nb-12 Zr-12 Sn) that is cost-effective and biocompatible, highlighting the efficiency of ML in accelerating alloy design for specific applications [76]. A systematic framework was proposed for phase prediction in HEAs, utilizing a genetic algorithm to select ML models and material descriptors. This optimized classification model achieved high accuracy in identifying solid-solution HEAs and their phases, demonstrating the efficacy of combining genetic algorithms with active learning approaches for iterative model improvement [77]. In response to the global challenge of diagnosing respiratory infections, a study combined surface-enhanced Raman spectroscopy (SERS) with ML for virus detection. This method enabled rapid, accurate classification of various viruses, highlighting the potential of ML in enhancing diagnostic techniques for timely and effective epidemic response [78]. The integration of multi-scale simulations and machine learning in researching HEAs was examined, addressing challenges like data quality and model interpretability. This approach highlighted the importance of combining experimental and theoretical research to enhance the precision of multi-scale simulations and the interpretability of ML models [79]. Using synchrotron microfocus X-ray diffraction, researchers explored a machine-learning-based approach to quickly predict fiber orientation metrics in biological composites. This method, validated with real experimental data, showed promise for automating data processing, as well as accelerating the analysis of large datasets in biomaterials research [80]. Figure 11: The diagram illustrates (**a**) the CNN training flowchart for predicting composite toughness and strength, (**b**) MSE convergence over epochs, and regression values for (**c**) toughness and (**d**) strength with blue (training) and red (testing) dots. The figure is reproduced from [73]. Another study aimed to identify suitable biomaterials for bone scaffolds using machine learning models, particularly ANNs. The research effectively ranked biomaterials, demonstrating high accuracy in selecting candidates for bone tissue engineering, thus illustrating the potential of ML in biomaterial selection and prioritization [81]. Lastly, an innovative approach using a CNN was introduced to generate spinodal structures for biomaterials, ideal for bone repair due to their stochastic and interconnected pore channels. As shown in Figure 13, the proposed model provided flexibility and computational efficiency, improving the development of stochastic biomaterials for biomedical applications [82]. Figure 12: Schematic diagram of machine-learning-assisted Ti alloy discovery. The figure is reproduced from [76]. Figure 13: Scheme for CNN-based high-throughput screening for spinodal structures integrates two CNNs: one for rapid spinodal structure generation and another for ultrafast property prediction, enabling efficient generation and screening of spinodal structures with target anisotropic elasticity. The figure is reproduced from [82]. The applications of AI and ML in composite materials research, as detailed in Table 4, illustrates a paradigm shift in the field. These technologies enable unprecedented precision and efficiency in material design, structural health monitoring, manufacturing optimization, and sustainability efforts. By leveraging AI and ML, researchers can rapidly innovate and improve material properties, leading to more advanced and capable composites. This integration not only enhances current practices but also opens up new possibilities for future research and development, ensuring that composite materials continue to evolve and meet the demands of various high-tech industries.