AI-Enabled UAV–IoT Framework for Autonomous Diagnostic Data Acquisition, Medicine Inventory Monitoring, and Telemedicine Support in ResourceConstrained Rural Healthcare Systems

Abstract

Access to timely and quality healthcare remains a significant challenge in rural and resource-constrained regions, where hospitals often lack advanced diagnostic infrastructure, real-time monitoring systems, and efficient medical supply chains. Recent advancements in Unmanned Aerial Vehicles (UAVs), Artificial Intelligence (AI), and Internet of Things (IoT) technologies present promising opportunities to address these limitations. However, most existing solutions focus on isolated functionalities such as drone-based medical delivery or IoT-based patient monitoring, without offering a unified and intelligent healthcare support system. This research proposes an AI-enabled UAV–IoT integrated framework designed to support autonomous diagnostic data acquisition, medicine inventory monitoring, and telemedicine services in rural healthcare environments. The proposed system utilizes IoT-enabled medical devices deployed at rural health centers to collect patient diagnostic data, which is transmitted through UAV-assisted communication or edge gateways to centralized healthcare facilities. UAVs equipped with autonomous navigation capabilities are employed not only for medical supply delivery but also for on-demand data collection and emergency response. Artificial intelligence techniques are incorporated to enable intelligent decision-making, including optimized route planning, priority-based medical delivery, and predictive analysis of healthcare demands. Additionally, the framework introduces a dronebased medicine inventory monitoring mechanism that tracks stock levels at rural hospitals and automatically schedules replenishment using optimized multi-drone coordination strategies. The proposed architecture is being evaluated using simulation tools such as ROS, Gazebo, and network modeling platforms to validate system performance in terms of latency, delivery efficiency, and data reliability. Preliminary results indicate that the integrated approach can significantly enhance healthcare accessibility, reduce response time in emergencies, and improve the efficiency of telemedicine services.