Current progress of digital twin construction using medical imaging

Abstract

Medical imaging is fundamental to digital twin technology, enabling patient-specific virtual models of anatomy and physiology. By integrating high-resolution modalities (Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ultrasound) with computational frameworks, recent imaging advances now support real-time simulation, predictive modeling, and earlier disease detection. Such capabilities directly inform individualized treatment planning and contribute to more precise, personalized care. Despite remaining challenges—complex anatomical modeling, multimodal integration, and high computational demands—recent advances in imaging and machine learning have significantly enhanced the accuracy and clinical utility of digital twins. The main contributions of our review are: (1) a system-by-system classification of methodologies; (2) evidence that advanced imaging modalities have improved diagnostic accuracy, treatment effectiveness, and patient outcomes beyond conventional approaches; and (3) identification of remaining technical bottlenecks. We further analyze key technical barriers—such as data scarcity and computational complexity—and outline future directions (e.g., AI-driven data augmentation, real-time model optimization) to unlock digital twins’ full potential in precision medicine.