Advancements and prospects of deep learning in biomaterials evolution

Abstract

In recent decades, significant strides have been made in advancing biomaterials for biomedical applications. Ideal biomaterials necessitate suitable mechanical properties, excellent biocompatibility, and specific bioactivities. However, the design and preparation of materials with these essential characteristics pose formidable challenges, persisting as significant issues in the field. The development and optimization of high-performance biomaterials, along with the construction of composites and hybrids with diverse biofunctions, present promising strategies for enhancing therapeutic and diagnostic procedures. However, reliance on traditional “trial and error” methods for acquiring a substantial volume of experimental data proves to be laborious, time consuming, and unreliable. An emerging and promising approach involves the successful application of artificial intelligence (AI), specifically deep learning (DL), to investigate and optimize the preparation and manufacturing techniques for various biomaterials. DL, as an automated and intelligent tool within the AI domain, finds widespread application in devising, analyzing, and optimizing different biomaterials. Through the “experiment-AI” technique, DL predicts the potential feature information and performance of biomaterials, showcasing remarkable potential in biomaterial research and development. This review comprehensively explores the application of DL-based technologies in the biomedical field, emphasizing cutting-edge advantages and providing insights and recommendations to enhance the efficacy of such approaches in biomaterials.