DIGITAL TWIN INTELLIGENCE IN ONCOLOGY: AI-POWERED VIRTUAL PATIENTS FOR PERSONALIZED TREATMENT OPTIMIZATION

Abstract

Digital twin intelligence is emerging as one of the most transformative innovations in precision oncology by creating continuously evolving virtual representations of individual cancer patients that integrate multimodal biomedical information to support personalized diagnosis, therapeutic optimization, disease monitoring, and clinical decision-making. Unlike conventional predictive models, digital twins continuously synchronize with real-world patient data, enabling dynamic simulation of disease progression, treatment response, toxicity, recurrence, and survivorship. Recent advances in foundation artificial intelligence (AI) models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement 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, biomedical literature, and longitudinal clinical outcomes into adaptive computational patient models. These intelligent virtual patients support biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive clinical trials, digital biomarker development, and evidence-based multidisciplinary decision support. Emerging technologies including multimodal large language models, agentic AI, federated learning, explainable artificial intelligence, retrieval-augmented generation, cloud-native healthcare infrastructure, and digital health ecosystems further strengthen digital twin intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational oncology. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of digital twin intelligence in oncology, emphasizing AI-powered virtual patients for personalized treatment optimization across the cancer care continuum.