Multi-agent clinical AI architecture reduces computational demands 65x compared to single-agent while maintaining performance under heavy workload

Mount Sinai's peer-reviewed study distributed healthcare AI tasks (patient information retrieval, clinical data extraction, medication dose checking) among specialized agents versus a single all-purpose agent. The multi-agent architecture reduced computational demands by up to 65x while maintaining or improving diagnostic accuracy. Critically, multi-agent systems sustained quality as task volume increased, while single-agent performance degraded under heavy workload. The architectural principle mirrors clinical care team specialization: each agent optimized for its specific task performs better than one generalist attempting everything. This is the first peer-reviewed demonstration of multi-agent clinical AI entering healthcare deployment at scale. The efficiency gain is large enough to drive commercial adoption independent of safety considerations.