EdgeCareRT: A Real-Time Federated Generative AI Framework for Clinical Decision Support in Mobile and Remote Healthcare Settings

Abstract

Clinical Decision Support Systems (CDSS) often face challenges in latency, data privacy, and resource limitations-especially in mobile and remote healthcare settings. This paper proposes EdgeCareRT, a real-time federated Generative AI framework optimized for edge-based clinical inference. The framework integrates lightweight transformer architectures with privacy-preserving federated learning to process multilingual and multimodal patient data without requiring central pooling. Comprehensive evaluations were conducted on two benchmark datasets: MIMIC-III for structured clinical records and MedDialog for multilingual conversational health data. EdgeCareRT achieved an F1-score of 0.91 for sepsis prediction and a ROUGE-L score of 0.70 for multilingual summarization, while ensuring differential privacy guarantees (ε = 4.8) and reducing communication overhead by 40% compared to conventional federated baselines. These results confirm its ability to balance accuracy, efficiency, and privacy in real-world healthcare applications. Beyond performance, EdgeCareRT emphasizes deployment feasibility in bandwidth-constrained and mobile-first environments. The framework demonstrates that federated Generative AI can effectively bridge disparities in access to advanced diagnostics and empower clinical decision-making at the edge. Its scalability and explainability make it suitable not only for hospital networks but also for rural and global public health scenarios where trustworthy real-time decision support is urgently needed.

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