Abstract: Traditional repair methods for peripheral neuropathies, such as autologous and allogeneic nerve grafts, face limitations, while peripheral nerve regeneration materials have emerged as a promising alternative. However, current biomaterials are mostly single-functional and insufficient in modulating the regenerative microenvironment. This review explores the application of artificial intelligence in the development of neural regenerative biomaterials, focusing on material design, performance prediction, and virtual experiments. Artificial intelligence has the potential to optimize material properties through machine learning and deep learning, predict material performance, and enhance nerve regeneration. Recent studies have demonstrated the ability of artificial intelligence to design biomaterials with improved biocompatibility and mechanical properties, as well as to accurately predict outcomes of nerve regeneration. However, several challenges remain, such as data integration, algorithm complexity, and ensuring clinical translation. The promising future of intelligent research and development in biomaterials lies in personalized treatment strategies, coupled with the integration of advanced technologies such as artificial intelligence and 3D bioprinting, to create more efficient neural repair materials. This review highlights the transformative potential of artificial intelligence in advancing peripheral nerve repair and improving patient outcomes. 

Key words: 3D printing, artificial intelligence, biomaterials, bioresorbable scaffolds, deep learning, machine learning, nerve conduits, nerve regeneration, peripheral nerve injuries, tissue engineering