Precision Immuno-Oncology Through Foundation AI Models: Integrating Immune Profiling, Multi-Omics, and Clinical Data

Abstract

Immunotherapy has revolutionized cancer treatment by harnessing the host immune system to recognize and eliminate malignant cells. Despite remarkable clinical success across multiple malignancies, only a subset of patients experiences durable therapeutic responses because of the extraordinary biological complexity of tumor–immune interactions. Recent advances in foundation artificial intelligence (AI) models have introduced powerful computational frameworks capable of integrating immune profiling, radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, electronic health records, and longitudinal clinical information into unified patient-centered representations that support precision immunooncology. Unlike conventional machine learning systems designed for isolated predictive tasks, foundation AI models learn generalized biomedical representations through self-supervised learning that can be adapted across numerous oncology applications including immunotherapy response prediction, biomarker discovery, molecular characterization, adaptive treatment planning, digital twins, clinical decision support, and personalized therapeutics. Advances in transformer architectures, multimodal learning, graph neural networks, retrieval-augmented generation, large language models, agentic AI, and generative artificial intelligence have substantially accelerated the development of intelligent immuno-oncology ecosystems capable of comprehensive biomedical reasoning. Nevertheless, important challenges remain regarding multimodal data harmonization, computational scalability, explainability, interoperability, cybersecurity, regulatory validation, and equitable implementation. This review provides a comprehensive overview of precision immuno-oncology through foundation AI models, emphasizing computational principles, clinical applications, implementation challenges, and future perspectives for integrating immune profiling, multi-omics, and clinical intelligence.[1]