Recent advancements in three dimensional (3D) printing technologies have transformed both industrial practices and everyday applications. In the biomedical domain, 3D bioprinting at the cellular and tissue levels has emerged as a promising approach with significant potential. Although machine learning (ML) has been successfully applied in various aspects of conventional 3D printing, including process optimization, dimensional accuracy analysis, defect detection, and material property prediction, its adoption in the context of 3D bioprinting remains limited. This review examines the current ML techniques used in traditional 3D printing and explores their potential contributions to the development of bioprinting technologies. Notably, existing studies have demonstrated up to a 25% improvement in dimensional accuracy and a 30% reduction in printing time when ML is applied to scaffold optimization. We argue that the integration of ML could significantly influence the future development of 3D bioprinting, opening new avenues for innovation in biomedical engineering.
Science Journal 