An AI-Enabled Autonomous Connected Ambulance Protocol for eHealth in Smart Cities

Abstract

Artificial Intelligence (AI)-enabled technologies have transformed emergency medical services (EMS) by introducing autonomous connected ambulances that optimize routes, monitor patients in real-time, and integrate with eHealth systems in smart cities. This review explores AI protocols, including the Autonomous Connected Ambulance-Route Resource Recommendation (ACA-R3) protocol, and their applications in enhancing response times, resource allocation, and patient outcomes in urban and rural settings. We discuss AI algorithms, edge computing integration, and performance metrics with a focus on reducing delays and improving healthcare efficiency. Our approach combines literature synthesis with case studies on AI-driven ambulance deployments. Applications in traffic-congested cities, remote areas, and telemedicine demonstrate the protocol's adaptability and connectivity. Traditional EMS systems often face 30-50% delays due to traffic, but AI-enabled protocols reduce response times by 40-60% while achieving 90-95% accuracy in patient triage. Remaining challenges include data privacy, infrastructure costs, and regulatory compliance. This work underscores AI's potential to foster sustainable, intelligent EMS in smart cities, promoting equitable access to emergency care.