Multi-Agent Agentic AI for Real-Time Clinical Decision Support and Healthcare Security

Abstract: Given the fast expansion of the digital healthcare frameworks, the need for cognitive clinical decision support is evident; however, vital medical frameworks are prone to highly sophisticated cyberattacks. By enabling collaborative clinical analysis, security monitoring, medical knowledge acquisition, risk assessment, and decision-making validation when several specialized AI agents cooperate in real-time, Multi-Agent Agentic Artificial Intelligence (MAAI) framework develops an independent, collaborative, and adaptive framework. In order to increase the precision of diagnosis, reaction rate, privacy assurance, and resilience of the operations, we propose Multi-Agent Agentic AI framework in this paper, which combines clinical intelligence with healthcare cybersecurity. Respecting the standards of healthcare, the proposed architecture enables secure data transmission from wearable devices, cloud-edge infrastructures, laboratory frameworks, and EHRs. Unlike existing AI-driven healthcare systems, experimental evaluation indicates improvements in terms of resource management, detection efficiency, prediction performance, and decision delay. The proposed architecture provides the basis for future intelligent clinical environments by offering reliable, interpretable, and secure healthcare services to hospitals, telemedicine frameworks, and smart healthcare ecosystems.

Keywords: Multi-Agent AI, Agentic AI, Clinical Decision Support, Healthcare Security, Electronic Health Records, Cybersecurity, Explainable AI, Medical AI, Edge Computing, Healthcare Analytics