Pan-Cancer Multimodal Intelligence: Integrating Radiomics, Pathomics, Genomics, and Electronic Health Records

Abstract

Cancer represents a heterogeneous group of diseases characterized by diverse molecular alterations, complex tumor microenvironments, variable therapeutic responses, and distinct clinical outcomes across different organ systems. Although advances in molecular biology, targeted therapeutics, immunotherapy, and precision medicine have significantly improved cancer management, effective individualized care increasingly depends upon comprehensive integration of heterogeneous biomedical information rather than isolated diagnostic modalities. Recent developments in artificial intelligence (AI) have introduced the concept of pan-cancer multimodal intelligence, which integrates radiomics, digital pathomics, genomics, transcriptomics, proteomics, laboratory biomarkers, electronic health records (EHRs), and longitudinal clinical data into unified computational frameworks capable of supporting precision oncology across multiple cancer types. Machine learning, deep learning, transformer architectures, graph neural networks, multimodal foundation models, self-supervised learning, retrievalaugmented generation, and generative AI have substantially enhanced multimodal biomedical representation learning while improving diagnosis, prognostic prediction, molecular characterization, therapeutic optimization, immunotherapy response prediction, digital twins, and intelligent clinical decision support. Despite remarkable technological progress, important challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, regulatory validation, and equitable implementation. This review provides a comprehensive overview of pancancer multimodal intelligence, emphasizing computational principles, clinical applications, implementation challenges, and future perspectives for precision cancer medicine.[1