Artificial intelligence-enabled bioprinting for tumor modeling: from biomimetic microenvironment reconstruction to drug screening

Recent advances in artificial intelligence (AI) and three-dimensional (3D) bioprinting have provided new strategies for developing in vitro tumor models with improved representation of native tumor microenvironments. This review examines the role of AI across the entire bioprinting workflow—from image-based model design and real-time printing control to post-printing functional assessment and high-throughput drug evaluation. We first outline how machine learning and deep learning algorithms optimize bioink formulation, predict the mechanical behavior of printed constructs, and enable error correction during fabrication. We then discuss AI-driven strategies for reconstructing key components of the tumor microenvironment, including extracellular matrices with tunable mechanical properties, perfusable vascular networks, and spatially organized multi-cellular architectures that incorporate cancer cells, stromal cells, and immune populations. The review further addresses the application of AI-enhanced bioprinted tumor models in automated drug screening, resistance mechanism studies, and personalized medicine, with particular attention to the integration of patient-derived cells and multi-omics data. We also evaluate current challenges, including the trade-off between biomimetic fidelity and throughput, the lack of standardized protocols and data repositories, and the need for improved model validation against clinical outcomes. Finally, we discuss emerging directions such as closed-loop optimization systems, intelligent bioinks, and the integration of bioprinting with organ-on-a-chip platforms. By providing a comprehensive overview of this rapidly evolving field, this review aims to inform researchers and clinicians about the current capabilities and future potential of AI-assisted bioprinted tumor models for oncology research and therapeutic development.