Multiscale Cancer Intelligence: Integrating Cellular, Molecular, Imaging, and Clinical Data Using Artificial Intelligence

Abstract

Cancer is a highly complex, multiscale biological disease involving dynamic interactions across molecular, cellular, tissue, organ, and patient levels. Traditional analytical approaches frequently investigate these biological scales independently, limiting comprehensive understanding of tumor evolution and therapeutic response. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have enabled the integration of heterogeneous biomedical information across multiple biological and clinical scales into unified computational frameworks. Multiscale cancer intelligence combines genomics, epigenomics, transcriptomics, proteomics, metabolomics, single-cell sequencing, spatial biology, digital pathology, radiological imaging, laboratory biomarkers, electronic health records, wearable technologies, and longitudinal clinical outcomes to generate continuously evolving patient-specific computational representations. These intelligent systems facilitate precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, digital twins, and clinical decision support while advancing systems oncology and personalized medicine. Recent developments in large language models, agentic AI, federated learning, reinforcement learning, retrieval-augmented generation, and explainable artificial intelligence have further expanded the capabilities of multiscale computational oncology. Despite remarkable progress, significant challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, regulatory validation, ethical governance, and equitable implementation. This review provides a comprehensive overview of multiscale cancer intelligence, highlighting computational principles, technological advances, clinical applications, implementation challenges, and future perspectives for integrating cellular, molecular, imaging, and clinical data using artificial intelligence.[1]