# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 1 Introduction  
AI and ML has become a powerful tool in various scientific fields, and its applications have permeated different industries. In recent years, the biomaterial field has also harnessed the potential of machine learning to advance research and development [1, 2]. This review article aims to explore the applications of ML in the biomaterial field, focusing on its impact, challenges, and future prospects. Biomaterials are essential in medicine, facilitating the creation of innovative medical devices, tissue engineering frameworks, and drug delivery mechanisms. The intricate relationship between biomaterials and living tissues requires a deep understanding of their properties, interactions, and performance. ML provides an avenue to enhance this understanding and improve the design and optimization of biomaterials [3]. One key application of ML in the biomaterial field is predictive modeling. By training algorithms on extensive datasets, researchers can develop models capable of predicting the behavior or characteristics of biomaterials. For example, ML algorithms can be trained to predict the biocompatibility of a material or forecast its degradation rate. These predictive models aid in the selection of biomaterials for specific applications, reducing the timeand cost associated with trial-and-error experiments [4; 5; 6; 7]. ML has also made notable advancements in the design and synthesis of biomaterials [8]. Traditional trial-and-error methods can be time-consuming and resource intensive. ML algorithms can expedite this process by analyzing large databases of material properties, identifying correlations, and generating new material designs with tailored properties [9]. This ability to generate novel biomaterials opens avenues for innovation and accelerates the discovery of materials with enhanced biocompatibility and performance [10]. ML also plays a critical role in image analysis and interpretation in the biomaterial field. Medical imaging techniques produce vast amounts of complex data, which can be challenging to interpret accurately. By utilizing ML algorithms, researchers can analyze and classify images, aiding in the diagnosis, monitoring, and treatment of diseases. Additionally, ML algorithms can be applied to image-based analysis of biomaterials, enabling rapid evaluation of material performance and integrity [11]. ML offers significant benefits in biomaterials research, yet it faces challenges such as limited data availability and quality. Developing robust ML models requires standardized, large-scale datasets, and expertise in model interpretation and validation due to their complexity. Ethical considerations, including data privacy and bias, are critical in healthcare applications. Looking forward, ML's future in biomaterials research promises advancements as datasets improve, enhancing model accuracy and reliability. Moreover, integrating ML with emerging technologies like nanotechnology and bioinformatics will synergistically advance biomaterials research [12; 13]. In the upcoming sections, we explore quick explanations of key AI and ML subfields, such as SL, US, RL, and DL. Following this, we delve into various ML methods commonly employed in biomaterials research. 1. Artificial Intelligence and Machine Learning Methods  
AI involves the emulation of human intellect in machines designed to replicate human thinking and actions. These advanced systems can carry out tasks that usually need human intelligence, like visual interpretation, speech understanding, decision making, and translating languages. AI includes various subfields such as machine learning, natural language processing, robotics, and computer vision, among others [14]. Figure 1 illustrates the hierarchical relationship between AI, ML, and DL. AI includes a wide array of technologies aimed at replicating human intelligence, such as ML, which uses algorithms to allow computers to learn from data and make predictions. A specific branch of ML and DL utilizes multi-layered neural networks to examine different aspects of data.