Artificial Intelligence and Digital Twin Networks for Predictive and Preventive Precision Oncology

Abstract

Precision oncology is undergoing a paradigm shift from reactive disease management toward predictive, preventive, and continuously adaptive cancer care through the convergence of artificial intelligence (AI) and digital twin technologies. Conventional oncology relies largely on episodic clinical assessments and static treatment strategies that often fail to capture the dynamic evolution of tumor biology and patient physiology. Digital twin networks, empowered by foundation AI models, multimodal transformers, graph neural networks, self-supervised learning, and generative artificial intelligence, enable the creation of continuously evolving virtual representations of patients that integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, spatial biology, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical outcomes. These computational ecosystems support biomarker discovery, therapeutic response prediction, adaptive treatment optimization, recurrence monitoring, preventive risk assessment, intelligent clinical decision support, and systems oncology while accelerating personalized medicine. Recent advances in large language models, reinforcement learning, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI have further expanded the capabilities of digital twin networks by enabling collaborative, privacy-preserving, and continuously learning computational frameworks. Despite remarkable technological progress, important challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, regulatory validation, ethical governance, and equitable implementation. This review provides a comprehensive overview of artificial intelligence and digital twin networks for predictive and preventive precision oncology, emphasizing computational principles, clinical applications, implementation challenges, and future perspectives for intelligent virtual oncology ecosystems.[1]