MULTIMODAL LEARNING IN PRECISION ONCOLOGY: BRIDGING IMAGING, MOLECULAR BIOLOGY, AND CLINICAL INTELLIGENCE

Abstract

Multimodal learning has emerged as one of the most transformative paradigms in precision oncology by enabling artificial intelligence (AI) systems to integrate heterogeneous biomedical data into unified computational representations that support comprehensive cancer diagnosis, prognostic prediction, therapeutic optimization, and personalized clinical decision-making. Conventional oncology frequently relies on isolated interpretation of radiological imaging, digital pathology, molecular diagnostics, and clinical information, limiting the ability to capture the complex biological interactions underlying tumor evolution and therapeutic response. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, 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 multimodal 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, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal learning in precision oncology, emphasizing the integration of imaging, molecular biology, and clinical intelligence as a transformative framework for personalized cancer medicine.