FOUNDATION AI MODELS FOR PRECISION IMMUNOTHERAPY: PREDICTING RESPONSE, RESISTANCE, AND CLINICAL OUTCOMES

Abstract

Immunotherapy has revolutionized modern oncology by enabling the immune system to recognize and eliminate malignant cells through immune checkpoint inhibitors, adoptive cellular therapies, cancer vaccines, bispecific antibodies, and emerging immunomodulatory strategies. Despite remarkable clinical success, therapeutic responses remain highly heterogeneous because of the extraordinary complexity of tumor–immune interactions, intratumoral heterogeneity, and dynamic evolution of the tumor microenvironment. Foundation artificial intelligence (AI) models have emerged as transformative computational frameworks capable of integrating heterogeneous biomedical information across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, immunomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific representations. These intelligent systems enable comprehensive prediction of immunotherapy response, resistance mechanisms, toxicity, recurrence, and survival while supporting biomarker discovery, digital twin simulation, adaptive treatment planning, and intelligent clinical decision support. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen precision immunotherapy by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of foundation AI models for precision immunotherapy, emphasizing prediction of therapeutic response, resistance, and clinical outcomes as a transformative paradigm in personalized oncology.