Multiagent AI Systems in Health Care: Envisioning Next-Generation Intelligence.

Background: Limited staff, rising costs, and regulatory oversight, coupled with the need to achieve clinical endpoints and improve access to care, has made scaling health care operations challenging. This article explores the emerging paradigm of multiagent artificial intelligence (AI) systems in health care, which represent a significant leap beyond traditional large language models. Observations: This analysis reviews the potential of multiagent AI systems to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. It describes a hypothetical sepsis management system comprising 7 specialized AI agents, with each agent handling specific aspects of patient care from data collection and diagnosis to treatment recommendations and resource management. Additional applications in chronic disease management and hospital patient flow optimization are also examined. The technical implementation of these systems is discussed, including the use of advanced large language models, interagent quality control measures, guardrail implementation, self-reflection mechanisms, integration with electronic health records, and the importance of explainable AI in ensuring decision transparency. Potential benefits include enhanced diagnostic accuracy and personalized treatment plans. Challenges remain related to data quality assurance, workflow integration, and ethical considerations. Future directions for AI include the integration of internet-enabled devices and the development of more sophisticated natural language interfaces. Conclusions: This article underscores the transformative potential of multiagent AI systems in health care while emphasizing the importance of rigorous validation, ethical oversight, and a patient-centered approach in their development and implementation.