Artificial intelligence for design strategies of tissue engineering materials

In recent years, artificial intelligence (AI), driven by machine learning (ML) and deep learning (DL) algorithms, has fundamentally transformed the design and performance prediction of tissue engineering materials. By analyzing large amounts of biomaterial data, ML has demonstrated remarkable effectiveness in optimizing mechanical properties, assessing biocompatibility, and enhancing the structural design of tissue engineering materials, while also accurately predicting the complex interactions between materials and cells. Due to its exceptional ability to process nonlinear data, DL is particularly effective in analyzing complex biological data, such as interactions between tissue engineering materials, cells, and proteins, as well as promoting tissue regeneration. This review systematically examines ML- and DL-based design methods for tissue engineering materials and their important applications in recent years. It also explores their technical advantages, challenges, and future directions, providing insights for advancing regenerative medicine.