Tumor biomaterials show great potential for targeted cancer therapy, yet their development and clinical translation have long been hampered by inefficient empirical trial-and-error models. These traditional methods cannot fully characterize the nonlinear relationships between a material’s physicochemical properties and its complex biological effects, nor can they resolve tumor heterogeneity—the primary cause of inconsistent clinical outcomes. This review systematically explores the application of artificial intelligence (AI) across the entire development pipeline of tumor biomaterials, from early rational material design to clinical treatment optimization. We show that AI addresses key bottlenecks in the field in four core ways: it speeds up novel material discovery via generative algorithms, accurately predicts the in vivo transport and uptake of materials, enables noninvasive and precise patient stratification, and optimizes synergistic combination treatment regimens. These advances form a data-driven closed-loop framework that connects preclinical research and clinical translation, overcoming the core limitations of traditional development models. We also outline key unresolved challenges, including data standardization, model interpretability, and regulatory compliance, and highlight AI’s growing role as a core driver of precision oncology and translational medicine.
Chaonan et al. (2026) studied this question.