Artificial Intelligence in the synthesis and application of advanced dental biomaterials: a narrative review of probabilities and challenges

Abstract This narrative review explores the role of Artificial Intelligence (AI) in advancing the synthesis, optimization, and clinical translation of dental biomaterials. It critically evaluates how AI-driven approaches address existing challenges related to material performance, biocompatibility, and durability, while identifying current research gaps and outlining future perspectives. A comprehensive literature synthesis was conducted through electronic searches in PubMed, Scopus, Web of Science, and Google Scholar. Selected articles encompassed both experimental and computational studies. Case studies were analyzed to illustrate the application of AI techniques, including machine learning (ML) for predicting mechanical properties, deep learning (DL) for microstructural and imaging analysis, and optimization algorithms ( e.g. , genetic algorithms) for materials discovery and formulation enhancement. The findings indicate that ML models can accurately predict mechanical properties ( e.g. , flexural modulus), DL enables automated detection of material degradation, and optimization algorithms accelerate the discovery of advanced biomaterials such as high-entropy ceramics. AI also facilitates the development of personalized, adaptive materials, including pH-responsive and self-healing resin composites. Nonetheless, barriers remain, including data scarcity, limited generalizability, high computational demands, and lack of standardization. AI is poised to redefine the landscape of dental biomaterials by enabling smarter material design, improved performance prediction, and accelerated clinical translation. This review advances the field by integrating emerging computational approaches with practical needs and identifying unresolved challenges that hinder real-world adoption. Ultimately, leveraging AI alongside technologies such as 3D printing and digital twin systems can drive the development of next-generation biomaterials tailored to patient-specific clinical outcomes.