Biological tissue engineering with AI

Authors from the Division of Plastic and Reconstructive Surgery at Stanford, California, discuss the advancements, methodologies, and future prospects of Machine Learning (ML) and artificial intelligence in tissue engineering. ML methods, categorized by the autonomy of their learning processes, include supervised, unsupervised, and semi-supervised learning, with hybrid approaches becoming more prevalent.

ML accelerates biomaterial design by analyzing compound data, facilitating optimal biomaterial experimentation while reducing time and resources. For example, ML models analyze polymer chemistry data to identify material properties and scaffold fabrication conditions efficiently. Despite ML's potential, challenges remain. ML analyses must be interpreted cautiously, especially concerning biological relationships. Model interpretability is crucial, and functional validation of ML-identified correlations is necessary. ML revolutionizes tissue engineering by expediting biomaterial design, enabling system-level analyses, and enhancing the understanding of complex biological processes. ML-informed approaches drive the development of next-generation tissue engineering constructs, offering new insights for tissue regeneration and disease management.