Clinical Digital Twins in Oncology: From Concept to Clinical Translation

Abstract

Clinical digital twins have emerged as one of the most promising innovations in precision oncology, offering a transformative approach to individualized cancer diagnosis, treatment planning, disease monitoring, and survivorship care. A clinical digital twin is a continuously evolving virtual representation of an individual patient that integrates multimodal biomedical information—including clinical records, radiological imaging, digital pathology, genomic sequencing, molecular profiling, laboratory biomarkers, physiological monitoring, and longitudinal health data—to simulate disease progression and therapeutic response. Unlike conventional predictive models that analyze isolated datasets, digital twins continuously update patient-specific computational models as new clinical information becomes available, enabling adaptive and personalized clinical decision-making. Recent advances in artificial intelligence (AI), machine learning, deep learning, multimodal learning, graph neural networks, transformer architectures, foundation models, and generative AI have significantly accelerated the development of clinically applicable oncology digital twins. These intelligent systems demonstrate considerable potential in cancer diagnosis, radiogenomics, computational pathology, immunotherapy prediction, adaptive radiation therapy, surgical planning, drug discovery, clinical trial optimization, and real-time clinical decision support. Furthermore, integration with cloud computing, wearable technologies, federated learning, and electronic health records enables continuously learning healthcare ecosystems capable of supporting precision medicine throughout the cancer care continuum. Despite substantial progress, important challenges remain regarding data interoperability, computational complexity, explainability, cybersecurity, regulatory approval, ethical governance, and large-scale clinical implementation. This review discusses the evolution of clinical digital twins in oncology, emphasizing their computational architecture, current clinical applications, translational opportunities, and future role in personalized cancer care.[1]