AI-Powered Hybrid Healthcare Surveillance and Emergency Response System for Next-Generation Smart Cities
Year: 2026
Type: article
Abstract: The rapid growth of smart cities has increased the demand for intelligent healthcare systems that can provide continuous patient monitoring, early disease detection, and efficient emergency response. Conventional healthcare surveillance systems often face challenges such as delayed emergency services, fragmented medical data, limited interoperability, and inadequate real-time decision-making. To address these limitations, this paper proposes an AI-Powered Hybrid Healthcare Surveillance and Emergency Response System (AI-HHSERS) for next-generation smart cities. The proposed framework integrates the Internet of Medical Things (IoMT), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Edge Computing, Cloud Computing, Federated Learning, Explainable Artificial Intelligence (XAI), Digital Twin technology, and Geographic Information Systems (GIS) to provide intelligent healthcare monitoring and emergency management. Physiological data collected from wearable sensors, medical devices, Electronic Health Records (EHRs), smart cameras, and environmental sensors are analyzed using hybrid AI models to predict disease risks, detect health anomalies, and generate early warning alerts. Edge computing enables low-latency processing for real-time decision-making, while cloud computing supports large-scale data storage and long-term analytics. Federated Learning ensures privacy-preserving collaborative model training without sharing sensitive patient data, and Explainable AI enhances the transparency and reliability of clinical decisions. In emergency situations, the GIS-enabled response module identifies the nearest ambulance, optimizes routing based on real-time traffic conditions, and automatically notifies hospitals to improve patient care. The proposed framework is expected to improve disease prediction accuracy, reduce emergency response time, enhance healthcare interoperability, and strengthen data security. Furthermore, it supports smart hospitals, remote patient monitoring, elderly care, epidemic surveillance, and disaster management, making it a scalable and reliable healthcare solution for future smart city ecosystems.
Authors: Dr. N. Geetha, Mrs. C. Ushanandhini
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225232624
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