AI-Enabled IoT System for Smart Healthcare and Emergency Response in Developing Countries (Global Journal of Sciences, 2025)
This paper presents a novel AI-enabled Internet of Things (IoT) framework to  transform healthcare delivery and emergency response in developing countries.  The proposed system combines wearable sensing based on TinyML, deep  learning inference on-device, and triage powered by reinforcement learning in a  hierarchical edge–cloud architecture optimized for data privacy and low latency.  To enable adaptive edge–cloud orchestration for real-time analytics and  predictive diagnostics without jeopardizing patient data, federated learning is  used for decentralized, secure model training. By assessing vital signs,  geolocation, geographical location, and resource availability, a reinforcement  learning agent dynamically prioritizes emergencies, maximizing response times  and triage effectiveness. In a simulated rural Sub-Saharan African environment,  field validation produced diagnostic accuracy of over 93%, outperforming  conventional cloud-only systems in speed and efficiency. The user-friendly  interface facilitates multilingual, cross-modal communication through voice  confirmation, app alerts, and SMS for populations with low literacy and internet  access. The system's 2.3-second real-time warning latency makes it particularly  useful for tracking infectious diseases, chronic conditions, and maternal care.  This scalable, morally sound paradigm offers a guide for AI-driven health  innovation in underprivileged environments, and it is in line with the UN  Sustainable Development Goals (SDGs 3 and 9).
Keywords: Iot powered by AI Intelligent emergency; response; Predictive analytics federated learning Smart; healthcare Real-time patient