Artificial intelligence empowering innovative research and development in medical materials: prospects from model algorithm breakthroughs to precision transformation

作者：Kun Lu, Qunbo Ying, Yong Dai, Qiang Yan, Shutong Wang, Yue Wang, Feilong Wang, Gaoxiang Huang, Tao Wang, Fengyan Chen, Xuxiu Tao, Pingping Wang, Qian Fan, Chenyin Lin, Chenyin Lin, Xiang Liu · 发表于：MedMat. · 年份：2025 · DOI：10.1097/mm9.0000000000000038 · 被引用次数：2 · 研究领域：Machine Learning in Materials Science、3D Printing in Biomedical Research、Manufacturing Process and Optimization

Traditional medical material development relies on trial-and-error experimentation and lengthy clinical trials, resulting in prolonged cycles, high costs, and limited success rates. This model not only severely hampers research and development efficiency but also struggles to rapidly address the urgent demand for new materials in the medical field. Artificial intelligence (AI) technology, by integrating multimodal data with advanced algorithms, is breaking through this bottleneck. This paper systematically reviews the application progress of AI across the entire medical material development chain, focusing on 3 core scenarios: “AI-driven molecular material design,” “biocompatibility prediction,” and “personalized material customization.” Through comparative analysis of differences in technical approaches and methodological frameworks among global research groups, it deeply elucidates the key challenges currently facing the field and offers forward-looking perspectives. “biocompatibility prediction,” and “personalized material customization.” By comparing and analyzing differences in technical approaches and methodological frameworks among global research groups, it deeply elucidates key challenges in the field and prospectively outlines future directions for the convergence of AI and medical materials. This aims to provide a systematic framework for innovative development in medical materials.