INTELLIGENT CANCER DIGITAL TWINS: AI-DRIVEN VIRTUAL PATIENT MODELS FOR PREDICTIVE ONCOLOGY

Abstract

Intelligent cancer digital twins are emerging as one of the most transformative innovations in precision oncology by enabling continuously evolving virtual representations of individual patients capable of supporting predictive diagnosis, personalized therapeutic planning, adaptive disease monitoring, and evidence-based clinical decision-making. Conventional oncology frequently depends upon fragmented interpretation of radiological imaging, molecular diagnostics, pathological findings, and episodic clinical assessments, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in artificial intelligence (AI), foundation models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative AI have enabled seamless 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 adaptive virtual patient ecosystems. These intelligent digital twins continuously synchronize with evolving patient biology to support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, toxicity prediction, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen digital twin ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational 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 intelligent cancer digital twins, emphasizing AI-driven virtual patient models as a transformative framework for predictive oncology and personalized cancer medicine.