PRECISION CANCER INTELLIGENCE: LEVERAGING FOUNDATION MODELS, DIGITAL TWINS, AND MULTIMODAL LEARNING FOR CLINICAL TRANSLATION

Abstract

Precision cancer intelligence is emerging as a transformative paradigm in modern oncology through the convergence of foundation artificial intelligence (AI) models, digital twins, multimodal learning, and translational clinical intelligence. Conventional oncology frequently relies on fragmented diagnostic modalities and isolated interpretation of molecular, imaging, pathological, and clinical information, thereby limiting comprehensive understanding of the dynamic biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of 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 ecosystems support early cancer detection, biomarker discovery, molecular characterization, 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 precision cancer intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of precision cancer intelligence, emphasizing the convergence of foundation AI, digital twins, and multimodal learning for successful clinical translation and personalized cancer medicine.