DIGITAL TWIN–ENABLED PRECISION ONCOLOGY: INTEGRATING LONGITUDINAL PATIENT DATA, MULTI-OMICS, AND ARTIFICIAL INTELLIGENCE

Abstract

Digital twin technology is rapidly emerging as one of the most transformative innovations in precision oncology by enabling continuously evolving virtual representations of individual cancer patients capable of supporting predictive, personalized, and adaptive clinical decision-making. Traditional oncology frequently depends on 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, and generative AI have enabled seamless integration of longitudinal patient data, radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and real-world 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, reinforcement 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 digital twin–enabled precision oncology, emphasizing integration of longitudinal patient data, multi-omics, and artificial intelligence as a transformative framework for next-generation personalized cancer medicine.