FUTURE CANCER INTELLIGENCE: CONVERGING FOUNDATION AI, DIGITAL TWINS, MULTIMODAL LEARNING, AND PRECISION ONCOLOGY

Abstract

Cancer care is entering a new era driven by the convergence of foundation artificial intelligence (AI), digital twins, multimodal learning, and precision oncology into intelligent computational ecosystems capable of continuously modeling disease biology and supporting personalized clinical decision-making. Conventional oncology frequently depends upon fragmented diagnostic workflows, isolated biomarker interpretation, and episodic therapeutic assessment that inadequately reflect the dynamic biological evolution of malignant disease. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration 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 into unified patient-specific computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and digital twin simulation while continuously learning from evolving biomedical evidence. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance computational oncology by enabling collaborative, privacy-preserving, and adaptive biomedical intelligence. 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 future cancer intelligence, emphasizing the convergence of foundation AI, digital twins, multimodal learning, and precision oncology as a transformative paradigm for next-generation personalized cancer care.