INTELLIGENT DIGITAL TWINS IN ONCOLOGY: REAL-TIME VIRTUAL PATIENT MODELING FOR PERSONALIZED THERAPEUTIC DECISION-MAKING

Abstract

Digital twin technology is emerging as one of the most transformative innovations in precision oncology by enabling the creation of continuously evolving virtual representations of individual cancer patients capable of supporting real-time therapeutic decision-making. Conventional oncology frequently relies on episodic clinical assessments and fragmented diagnostic information that inadequately capture the dynamic biological evolution of tumors throughout diagnosis, treatment, recurrence, metastasis, and survivorship. Recent advances in artificial intelligence (AI), foundation models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative AI have enabled 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 models. These intelligent digital twins continuously synchronize with evolving patient biology, supporting 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 enhance digital twin ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite remarkable technological progress, significant 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 digital twins in oncology, emphasizing real-time virtual patient modeling for personalized therapeutic decision-making as a transformative paradigm for next-generation precision cancer care.