Artificial Intelligence–Driven Digital Twins in Precision Oncology: Transforming Personalized Cancer Diagnosis, Treatment, and Survivorship

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, precision medicine, and targeted therapeutics. The extraordinary biological heterogeneity observed among tumors, coupled with dynamic interactions between malignant cells, the immune system, and the tumor microenvironment, continues to challenge conventional treatment strategies. Recent developments in artificial intelligence (AI), computational modeling, and multimodal biomedical data integration have introduced the concept of digital twins as a promising framework for personalized oncology. A digital twin is a continuously updated virtual representation of an individual patient that integrates clinical, radiological, pathological, genomic, molecular, physiological, and longitudinal health data to simulate disease progression and therapeutic response. Unlike traditional predictive models that focus on isolated datasets, digital twins enable dynamic patientspecific modeling capable of supporting diagnosis, prognostic prediction, treatment optimization, toxicity assessment, and survivorship management. Advances in machine learning, deep learning, foundation models, graph neural networks, reinforcement learning, and generative AI have significantly enhanced the development of oncology digital twins by enabling real-time integration of heterogeneous biomedical information. These computational systems have demonstrated growing potential across tumor detection, radiogenomics, immunotherapy prediction, adaptive radiation therapy, surgical planning, clinical trial optimization, and precision drug development. Nevertheless, several challenges remain regarding data standardization, interoperability, computational complexity, ethical governance, model transparency, regulatory validation, and large-scale clinical implementation. This review provides a comprehensive overview of digital twin technologies in oncology, highlighting their computational foundations, current clinical applications, emerging innovations, and future role in advancing personalized cancer care.[1]