# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Metal Materials
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Categories** & **Applications** \\ \hline \multirow{2}{*}{Predictive modeling} & Corrosion resistance: Predicting corrosion behavior under different conditions. \\  & Mechanical properties: Forecasting tensile strength, hardness, and ductility. \\ \hline \multirow{2}{*}{Material design and discovery} & Alloy design: Discovering new alloys with desired properties. \\  & Additive manufacturing: Optimizing parameters for metal 3D printing processes. \\ \hline \multirow{2}{*}{Process optimization} & Heat treatment: Predicting outcomes to optimize time and temperature profiles. \\  & Casting and forging: Minimizing defects and optimizing process parameters \\ \hline \multirow{2}{*}{Failure analysis and prevention} & Fatigue and fracture: Analyzing data to predict failure points. \\  & Crack propagation: Modeling initiation and growth of cracks. \\ \hline \multirow{2}{*}{Surface engineering} & Coating design: Designing advanced coatings to enhance properties like wear resistance. \\  & Surface treatment: Optimizing treatments such as anodizing, plating, and chemical vapor deposition. \\ \hline \multirow{2}{*}{Subfields in biomaterials} & Biomedical implants: Titanium alloys and stainless steel for orthopedic and dental implants. \\  & Bioactive metals: Magnesium alloys for biodegradable implants; Nitinol for flexible medical devices. \\ \cline{1-1}  & Metallic scaffolds: Porous metals for tissue engineering applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AI and ML applications in metal materials. to optimize material properties during the fabrication process, thereby enhancing the overall efficiency and effectiveness of alloy production techniques [47]. In a demonstration of machine learning's prowess in materials science, [PERSON] et al. embarked on a quest to streamline the synthesis and characterization of metal-organic frameworks (MOFs). Through the judicious application of advanced ML methodologies, they successfully identified and prioritized potential candidates, expediting the discovery process and facilitating the exploration of novel MOF structures with diverse applications [48]. In the field of metals, QSAR modeling and ML techniques show promise in predicting cytotoxicity and other properties. These techniques can handle diverse data rapidly, especially in image analysis and phenotypic recognition. However, challenges such as data standardization and collaboration issues hinder the adoption of ML in these fields. Standardizing data collection and promoting collaboration among researchers are crucial steps to improve data sharing and fully utilize ML in biomaterials and tissue engineering research. In a comprehensive study focused on predicting cell viability in the presence of metal oxide nanomaterials, researchers developed quasi-QSAR models to assess toxicity and biocompatibility. This endeavor not only shed light on the potential health implications of exposure to these materials but also underscored the pivotal role of machine learning in advancing our understanding of nanotoxicology [49]. Exploring novel methodologies in transition metal chemistry, researchers pioneered the utilization of innovative topological representations, such as RAC (Ring Analysis with Connectivity). This groundbreaking approach aimed to enhance the predictive capabilities of machine learning models, enabling more accurate forecasts of various properties critical to the field of inorganic chemistry [50]. Through the development of a sophisticated machine learning model, researchers endeavor to predict the adsorption energies of CH4-related species on Cu-based alloys. This endeavor offered invaluable insights into the design of selective catalytic reactions, potentially revolutionizing the field of catalysis by facilitating the development of more efficient and sustainable processes [51].