Digital Twin for Neurological Condition: Systematic Scoping Review

Digital twin technology has emerged as a promising approach in healthcare by creating virtual representations of patients using real-world clinical and physiological data. By integrating multimodal data such as medical imaging, electronic health records, physiological signals, and wearable sensor data, digital twins can support disease monitoring, prognosis prediction, and personalized treatment planning. In neurological conditions, which are characterized by complex disease mechanisms and heterogeneous progression, digital twin approaches may provide new opportunities for improving diagnosis, prognosis prediction, and individualized therapeutic strategies. However, current research on digital twin applications in neurological diseases remains fragmented across disciplines including biomedical engineering, artificial intelligence, and clinical neurology. Therefore, a comprehensive mapping of existing studies is needed to understand how digital twin technologies are currently being applied to neurological conditions. This scoping review aims to map the existing literature on digital twin applications for neurological conditions, identify the types of neurological diseases studied, the data sources and modeling approaches used, and the reported clinical applications. The review will be conducted following the PRISMA-ScR guidelines.