Machine learning in tissue engineering: a comprehensive review of applications, challenges, and prospects

Tissue engineering, rooted in cell biology and materials science, focuses on repairing or replacing injured tissues and organs, offering groundbreaking solutions in regenerative medicine. Although progress has been made in scaffold design, growth factor utilization, and stem cell applications, challenges such as prolonged production timelines, high costs, and unpredictable tissue outcomes persist. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), which integrates computer science with large-scale data, have emerged as innovative tools with the potential to address these limitations. By leveraging ML and DL, researchers can optimize biomaterial selection, enhance scaffold fabrication, predict tissue growth, and improve organ transplantation outcomes. This paper explores the latest applications of ML and AI-driven DL approaches in tissue engineering, focusing on property prediction and optimization of biomaterials, scaffold design, tissue regeneration, and 3D bioprinting. Additionally, it highlights the challenges of data quality, model interpretability, and standardization, which currently hinder the full integration of these technologies. Despite these obstacles, the convergence of AI, ML, and DL with tissue engineering holds immense promises for accelerating scientific discoveries and improving therapeutic outcomes. This review aims to provide a comprehensive overview of current advancements, address existing challenges, and outline future directions, offering valuable insights for researchers to drive innovation in this interdisciplinary field.