# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Metal Materials  
The use of metals in various applications, including biomaterials, has seen significant advancements, particularly with the integration of ML and data mining techniques in recent years. Predictive modeling in corrosion resistance and mechanical properties, such as tensile strength and ductility, has greatly improved the reliability and performance of metal components. Material design and discovery have been enhanced through ML in alloy design and additive manufacturing, optimizing parameters for metal 3D printing processes. Process optimization has seen significant improvements, with ML models predicting outcomes of heat treatment and refining casting and forging processes by minimizing defects. AI also plays a crucial role in failure analysis and prevention, predicting fatigue and fracture points, and modeling crack propagation to aid in maintenance and design improvements. Surface engineering has benefited from ML in designing advanced coatings and optimizing surface treatments to achieve desired characteristics [42]. In the field of biomaterials, specific subfields have emerged, such as biomedical implants using titanium alloys and stainless steel, bioactive metals like magnesium alloys and Nitinol for biodegradable and flexible medical devices, and metallic scaffolds for tissue engineering, providing structures that support cell growth and tissue regeneration [43]. Table 2 highlights the broad range of applications and subfields where ML and AI are making substantial contributions to the field of metal materials. Researchers extensively explored the vast realm of chemical databases, including Citrination and The Materials Project, to meticulously examine the descriptors associated with metals. This comprehensive investigation aimed to unravel the intricate chemical properties inherent in different metal compositions, shedding light on their diverse behaviors and characteristics [44]. Delving into the realm of biomedical applications, scientists undertook a meticulous review of nanoparticles' predictive capabilities using advanced machine learning methodologies. Through the utilization of random forest classifiers, [PERSON] et al. achieved a remarkable level of accuracy in forecasting material behaviors relevant to biomedical contexts, signifying a significant leap forward in the field of material science [45]. The exploration of quantum-mechanical properties inherent in inorganic complexes prompted researchers to embark on a thorough examination of various feature selection methods. This rigorous investigation aimed to elucidate the complex structure-property relationships prevalent in transition metal chemistry, offering valuable insights into the fundamental mechanisms governing these intricate systems [46]. Leveraging the synergistic potential of cellular automata and machine learning, scientists endeavored to simulate the intricate processes underlying the growth of aluminum alloys. By meticulously analyzing the interplay between porosity and solidification parameters, researchers sought