Digital Twin-Based Framework for Early Prediction and Monitoring ofNeurological Disorders

Introduction: Neurological conditions, such as Parkinson’s disease, migraines, and stroke, continue to rise worldwide, generating foremost challenges for timely diagnosis and effective management. Digital Twin (DT) technology, which develops dynamic virtual representations of patients or their physiological systems, is increasingly being explored as a tool for advancing precision healthcare. Traditional approaches often face limitations in early, precise diagnosis and continuous monitoring, requiring proactive, personalized management strategies. These approaches rely on single-modality data or static health records. In order to overcome these limitations, a digital twin-based machine learning framework with multimodal data sources, real-time monitoring, and explainable AI is proposed, bridging the gap between data fragmentation, monitoring delays, and limited interpretability with advanced precision and patient-centered care. Methods: This review aimed to compile and examine research articles from the last decade that investigate the use of DT in neurological disorders. Studies were selected from leading databases and assessed for their contributions to diagnostic support, continuous monitoring, therapeutic refinement, and clinical applicability Results: This framework makes the management of large datasets easy, allows continuous monitoring of disease progression, and optimizes therapeutic interventions. Emergent trends highlight the integration of DT with machine learning for predictive analytics, development in multimodal and real-time data collection, and interest in clinical deployment. There is a broad consensus on the promise of DT to personalize care, improve treatment outcomes, and support proactive disease management. However, unresolved debates persist regarding the issues of data reliability, interoperability, privacy, ethical safeguards, and the lack of standardized frameworks, which continue to hinder the large-scale adoption of these technologies. Discussion: DT can also play a part in reducing healthcare costs through optimized resource allocation and pre-emptive care. Moreover, data quality, standardization, security, privacy, and ethical considerations emphasize the need for robust data governance and adherence to regulations like HIPAA17. Conclusion: Digital twin-driven machine learning framework brings out the profound potential of digital twin-driven machine learning in revolutionizing patient care for neurological disorders, thereby enhancing precision, personalization, and efficiency in diagnostics and monitoring. DT signifies a rapidly emerging method to neurological disease management, offering significant potential for precision, personalization, and efficiency. However, overcoming current technical and ethical challenges remains essential for their successful translation into clinical practice.