AI-Driven Digital Twins for Predictive Oncology and Precision Therapeutics

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, targeted therapies, immunotherapy, and precision medicine. The extraordinary biological heterogeneity of tumors, coupled with dynamic interactions among malignant cells, the immune system, and the tumor microenvironment, continues to challenge conventional treatment strategies. Recent advances in artificial intelligence (AI), computational biology, systems medicine, and multimodal biomedical data integration have introduced AI-driven digital twins as a transformative framework for predictive oncology and precision therapeutics. A digital twin is a continuously evolving virtual representation of an individual patient that integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, physiological monitoring, electronic health records, and longitudinal clinical information to simulate disease progression and therapeutic response. Unlike traditional predictive models that analyze isolated datasets, AI-driven digital twins continuously learn from multimodal patient data, enabling dynamic prediction of disease evolution, treatment efficacy, toxicity risk, and resistance mechanisms. Advances in machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, generative AI, and foundation models have substantially enhanced the development of intelligent digital twin ecosystems capable of supporting diagnosis, prognostic prediction, adaptive treatment planning, immunotherapy optimization, radiation therapy, surgical planning, and precision drug development. Although significant challenges remain regarding interoperability, data harmonization, explainability, cybersecurity, regulatory validation, and ethical governance, AI-powered digital twins are expected to become central components of future precision oncology. This review presents an overview of the computational foundations, clinical applications, technological innovations, and future directions of AI-driven digital twins in predictive oncology and personalized cancer therapeutics.[1]