Yani Chen, Ming Chen, Yiqing Xi, Yun Xia, Siyuan Ma, Baoxiang Chen
Artificial intelligence (AI) has become a pivotal driver of innovation in cancer immunotherapy through large-scale biological data modeling, multimodal information integration, and advanced machine learning approaches. At the AACR Annual Meeting 2026, AI-based frameworks demonstrated broad applications across the immunotherapy continuum, including response prediction, patient stratification, neoantigen identification, and therapeutic development. An array of studies has transcended conventional single-biomarker paradigms, leveraging multimodal clinical and molecular datasets to construct biologically interpretable representations of tumor-immune ecosystems. Taken together, these advances signal a shift from reductionist biomarker-based prediction toward integrative, data-driven precision immunotherapy, fundamentally reshaping therapeutic stratification and target discovery and establishing artificial intelligence as a central driver of the conceptual and practical transformation of cancer immunotherapy.