Machine learning approaches for optimizing bioprinting processes: A comprehensive review

Abstract

Bioprinting has emerged as a transformative technology for fabricating complex biological constructs with applications in tissue engineering, regenerative medicine, and drug delivery. However, the bioprinting process involves highly nonlinear, multiscale, and interdependent parameters, making traditional trial-and-error optimization inefficient and costly. In recent years, machine learning has gained significant attention as a powerful tool for data-driven optimization across different stages of the bioprinting workflow. This review provides a comprehensive overview of machine learning approaches for optimizing bioink formulation, rheological behavior, printing parameters, structural fidelity, cell viability, and post-printing tissue maturation. Various supervised, unsupervised, deep learning, and reinforcement learning techniques are discussed, along with their input data types, prediction targets, and performance advantages. Current challenges, including limited datasets, lack of standardization, and model interpretability, are critically analyzed. Finally, emerging trends such as physics-informed machine learning, closed-loop bioprinting systems, and digital twins are highlighted as promising directions toward fully autonomous and clinically translatable bioprinting platforms.