FOUNDATION AI MODELS FOR SYSTEMS ONCOLOGY: INTEGRATING MULTI-OMICS, IMAGING, AND CLINICAL INTELLIGENCE FOR PRECISION CANCER CARE

Abstract

Systems oncology is emerging as a transformative paradigm in precision cancer medicine through the integration of artificial intelligence (AI), multi-omics, medical imaging, digital pathology, and longitudinal clinical intelligence into comprehensive computational ecosystems capable of modeling cancer as a dynamic biological system. Conventional oncology often relies on fragmented analysis of individual diagnostic modalities, limiting the understanding of complex interactions among molecular pathways, tumor microenvironments, host immunity, and clinical outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and real-world clinical evidence. These intelligent systems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen systems oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of foundation AI models for systems oncology, emphasizing the integration of multi-omics, imaging, and clinical intelligence as a transformative framework for precision cancer care.