Accepted for/Published in: JMIR AI
Date Submitted: Nov 13, 2025
Open Peer Review Period: Nov 13, 2025 - Jan 8, 2026
Date Accepted: Mar 18, 2026
(closed for review but you can still tweet)
AI-Driven Digital Twin Architecture for Multimodal Prediction and Adaptive Intervention in Cognitive Aging
ABSTRACT
Age-related cognitive dysfunction, including mild cognitive impairment (MCI) and dementia, underscores the need for scalable and personalized predictive models. We present a conceptual digital twin (DT) framework powered by artificial intelligence (AI) to support early detection, real-time monitoring, and adaptive intervention. The system is structured around four core processes, Perception, Analytics, Decision-Making, and Adaptive Feedback, across five functional layers: Data, Integration, Modeling, Reasoning, and Application. Multimodal behavioral, physiological, and clinical data are harmonized using Fast Healthcare Interoperability Resources (FHIR) and Observational Medical Outcomes Partnership (OMOP) standards. Predictive modeling employs convolutional and recurrent neural networks, gradient boosting, and reinforcement learning. Designed for deployment via HIPAA-compliant cloud platforms (e.g., AWS, Azure), the framework supports seven application modules, including signal- and pose-based assessment, personalized mind–body training, cognitive rehabilitation, and disease trajectory simulation. This architecture provides a foundation for real-time, individualized, and trajectory-aware cognitive care, advancing the future of precision medicine in aging.
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