INTELLIGENT MULTIMODAL ONCOLOGY: INTEGRATING RADIOLOGY, PATHOLOGY, MULTI-OMICS, AND WEARABLE HEALTH DATA

Abstract

Intelligent multimodal oncology is emerging as a transformative paradigm in precision cancer medicine by integrating radiology, pathology, multi-omics, wearable health technologies, and foundation artificial intelligence (AI) models into unified computational ecosystems. Conventional oncology workflows frequently analyze radiological imaging, histopathology, molecular biomarkers, laboratory investigations, and physiological monitoring independently, limiting comprehensive characterization of tumor biology and personalized 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 seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support early diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, adaptive disease monitoring, immunotherapy selection, digital twin simulation, survivorship management, 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 multimodal oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, 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 intelligent multimodal oncology, emphasizing integration of radiology, pathology, multi-omics, and wearable health data as transformative technologies for next-generation precision oncology.