AI for bioactive materials: From material design to biological applications.

Abstract

Bioactive materials are engineered to actively interact with biological systems and induce favorable cellular and tissue responses. The development of bioactive materials is inherently challenging due to the complexity of the biological processes that they participate in. Traditional high-throughput platforms and computational simulations are often insufficient to address the multidimensional requirements. Artificial intelligence (AI) provides powerful tools to address these challenges. By utilizing data-driven models, AI can reveal non-obvious relationships within experimental datasets, enable prediction of material behaviors, and guide rational design of new materials. In this review, we provide a systematic overview of recent advances in AI for bioactive materials. Firstly, the classification of bioactive materials is summarized, including bioactive metals, bioactive ceramics, bioactive polymers and bioactive composites. Then, current AI tools employed in bioactive materials are presented according to various classification methods. Next, the process-oriented applications of AI in bioactive materials are introduced, such as material design, fabrication optimization, property prediction, in vitro assessments, and in vivo assessments. Finally, the key challenges and future opportunities in this field are discussed comprehensively.