The importance of biomaterials lies in their fundamental roles in medical applications such as tissue engineering, drug delivery, implantable devices, and radiological phantoms, with their interactions with biological systems being critically important. In recent years, advancements in deep learning (DL), artificial intelligence (AI), machine learning (ML), supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) have significantly transformed the field of biomaterials. These technologies have introduced new possibilities for the design, optimization, and predictive modeling of biomaterials. This review explores the applications of DL and AI in biomaterial development, emphasizing their roles in optimizing material properties, advancing innovative design processes, and accurately predicting material behaviors. We examine the integration of DL in enhancing the performance and functional attributes of biomaterials, explore AI-driven methodologies for the creation of novel biomaterials, and assess the capabilities of ML in predicting biomaterial responses to various environmental stimuli. Our aim is to elucidate the pivotal contributions of DL, AI, and ML to biomaterials science and their potential to drive the innovation and development of superior biomaterials. It is suggested that future research should further deepen these technologies’ contributions to biomaterials science and explore new application areas.

This study investigates the absorption of X-rays in mandibular tissues by comparing real tissues with tissue-equivalent materials using the PHITS Monte Carlo simulation program. The simulation was conducted over a range of X-ray photon energies from 50 to 100 keV, with increments of 5 keV, to evaluate the dose absorbed by different tissues. Real tissues, such as the skin, parotid gland, and masseter muscle, were compared with their tissue-equivalent polymeric materials, including PMMA, Parylene N, and Teflon. The results showed that the real tissues generally absorbed more X-rays than their corresponding equivalents, especially at lower energy levels. For instance, at 50 keV, differences in the absorbed doses reached up to 50% for the masseter muscle and its equivalent, while this gap narrowed at higher energies. The study highlights the limitations of current tissue-equivalent materials in accurately simulating real tissue behavior, particularly in low-energy X-ray applications. These discrepancies suggest that utilizing tissue-equivalent materials may lead to less accurate medical imaging and radiotherapy dose calculations. Future research should focus on improving tissue-equivalent materials and validating simulation results with experimental data to ensure more reliable dosimetric outcomes. This study provides a foundation for refining radiation dose calculations and improving patient safety in clinical applications involving X-rays.