Advancing biomaterial research with artificial intelligence

Biomaterials play a crucial role in the healthcare sector, particularly in applications involving implantation into the human body, where preventing adverse effects is of paramount importance. Artificial intelligence (AI) has become an essential tool in biomaterial research to accelerate the development and innovations of biomaterials across different applications while addressing challenges in fabrication and characterization processes that were traditionally time-consuming, labor-intensive, and costly. By leveraging AI, researchers can enhance the performance, efficiency, and scalability of biomaterial development. This review provides a detailed examination of the applications of Machine Learning and Deep Learning across various biomaterial categories, including polymeric, metallic, ceramic, and composite biomaterials. It also explores fundamental AI methodologies, including supervised, unsupervised, semi-supervised and reinforcement learning, highlighting their roles in tackling forward and inverse design problems. Beyond the benefits, the review discusses key limitations of AI, such as model interpretability, data quality, and overfitting, along with emerging solutions like Explainable Artificial Intelligence, integrating methods such as Sharpley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to address these challenges. Together, these insights underscore the potential and evolving landscape of AI in advancing biomaterial research.