Multiagent AI Systems in Healthcare

Multiagent AI systems represent a sophisticated solution to complex healthcare challenges by enabling coordinated action among autonomous agents. These systems can enhance diagnostic accuracy, optimize resource allocation, and support treatment planning through collaborative decision-making. This article examines the technical foundations of multiagent AI systems, including system architecture, communication protocols, and decision-making mechanisms. A prototype framework was developed using cooperative multiagent reinforcement learning (MARL) and Distributed Constraint Optimization Problems (DCOP), implemented in a simulated emergency department environment. Results showed improved task completion, faster convergence of learning strategies, and more efficient staff scheduling compared to rule-based systems. Communication efficiency was enhanced through the use of FIPA-ACL protocols and adaptive throttling. Multiagent AI systems hold significant promise for transforming healthcare delivery by increasing efficiency, robustness, and personalization in clinical work flows.