RemoteCare: AI-Driven Multimodal Predictive Framework With Blockchain for Personalized Remote Patient Monitoring in IoMT

The Internet of Medical Things (IoMT) enables continuous health monitoring but still faces challenges in achieving personalized predictions and ensuring secure, tamper-proof data integrity. We presentRemoteCare, an AI-driven multi-modal framework that fuses synchronized physiological and network data for dual-task learning, simultaneously performing personalized health state classification (Normal, Warning, Critical) and cyberattack detection in IoMT traffic. Unlike conventional population-based thresholds,RemoteCaredynamically adapts alerts to each patient’s baseline, thereby minimizing false alarms and enhancing clinical reliability. A hybrid CNN–GRU–LSTM architecture jointly captures spatial and temporal dependencies across heterogeneous signals, while SHAP-based explainability provides transparent, patient-specific insights into the features influencing each prediction. To guarantee auditability, all predictions are immutably recorded on thePureChainblockchain integrated with IPFS, ensuring decentralized and tamper-proof storage. Evaluated on the WUSTL-EHMS-2020 dataset (Enhanced Healthcare Monitoring System),RemoteCareachieved 99.7% accuracy for health classification and 96.0% for intrusion detection, with negligible false alarms and efficient inference suitable for real-time deployment. By unifying multimodal prediction, personalization, interpretability, and secure logging,RemoteCareestablishes a trustworthy framework for early intervention, patient-specific risk assessment, and clinician-oriented decision support in remote healthcare.