Swarm AI enables decentralized, self-organizing medical systems to produce collective solutions without central control. In these systems, multiple intelligent agents follow simple predefined rules, communicate with nearby agents, and respond to local environmental changes. Through these local interactions, complex and efficient group behaviour emerges. Because the system is distributed, the failure of individual agents does not disrupt overall performance, and it can continue to function effectively even as the number of agents changes. In medicine, swarm AI is applied to tasks such as medical image analysis, modelling biological processes, optimizing delivery routes, managing traffic, routing networks, balancing loads, clustering medical data, training neural networks, and supporting patient consultations and other healthcare services. It also makes it easier to program large swarms of more than 250 agents. Larry Greenblatt, Chief AI Scientist at Inter Network Defence, is developing a swarm-based, multi-model, multi-system architecture aligned with the ISO seven-layer OSI model for use in healthcare management, organization, and clinical practice.
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