INTEGRATIVE CANCER INTELLIGENCE: FOUNDATION MODELS FOR RADIOMICS, PATHOMICS, MULTI-OMICS, AND DIGITAL HEALTH

Abstract

Integrative cancer intelligence is emerging as a transformative paradigm in precision oncology by combining foundation artificial intelligence (AI) models with multimodal biomedical data derived from radiomics, pathomics, multi-omics, and digital health technologies. Traditional oncology workflows often analyze radiological imaging, histopathology, molecular biomarkers, laboratory investigations, and clinical records independently, limiting comprehensive characterization of tumor biology and individualized therapeutic decision-making. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, digital biomarkers, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support early diagnosis, 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, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and digital health ecosystems further strengthen computational oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of integrative cancer intelligence, emphasizing foundation models for radiomics, pathomics, multi-omics, and digital health as transformative technologies for next-generation precision oncology.