Deep Learning and Engineered Biomaterials‐Enhanced Imaging‐Guided Cancer Immunotherapy

ABSTRACT The clinical success of immunotherapies is hindered by the absence of robust methodologies for the early and accurate assessment of antitumor immune responses. Integrating engineered biomaterials with artificial intelligence (AI)‐driven molecular imaging technology offers a powerful approach for predicting immune efficacy. However, its clinical translation is significantly hindered by several critical limitations, including the complex and costly synthesis of multifunctional imaging probes, insufficient deep‐tissue penetration, suboptimal in vivo signal‐to‐noise ratios, potential immunogenicity and off‐target toxicity, and the limited generalizability of deep learning models across heterogeneous patient populations. In view of these pressing scientific challenges, this review focuses on the application of chemical and biomolecular strategies‐enhanced engineered biomaterials in promoting the sensitivity and specificity of advanced imaging modalities for visualizing signaling molecules and immune cells in the tumor microenvironment (TME). Meanwhile, recent progress in deep learning‐driven image analysis has enabled strong predictive capabilities and automated the understanding of complex immune system dynamics. Together, these technologies provide a transformative platform for non‐invasive monitoring, personalized treatment, and response prediction, with the potential to advance precision immuno‐oncology and significantly impact both basic research and clinical translation.