Researchers at the Icahn School of Medicine at Mount Sinai announced on March 10 that health care artificial intelligence (AI) systems perform better when tasks are distributed among multiple specialized agents, rather than relying on a single, all-purpose model. The findings were published in the March 9 online issue of npj Health Systems.
The study is significant as AI becomes more common in health care, managing everything from records to medication decisions. Understanding how these systems handle high workloads is important for hospitals and clinics looking to scale up their use of AI without sacrificing quality or efficiency.
According to senior study author Girish N. Nadkarni, "For health care organizations, our findings point to a smarter way to use AI. By assigning different tasks, such as finding patient information, extracting data, or checking medication doses, to specialized AI agents, systems can run faster and more reliably while keeping costs under control. Ultimately, this kind of design could help health care teams spend less time on administrative work and more time focusing on patients."
The research team compared two approaches: one using a single system for many clinical tasks and another using a network of specialized agents managed by an orchestrator. They tested both methods with state-of-the-art language models across common clinical functions like information retrieval and medication dosing calculations under simulated real-world conditions involving up to 80 simultaneous tasks.
Lead author Eyal Klang said, "What we found is that AI systems behave a lot like people. When you ask one system to do too many different things at once, performance suffers. But when one orchestrator agent divides the work among specialized agents, the system stays accurate, responsive, and far more efficient, even under heavy demand." The coordinated multi-agent system maintained higher accuracy while using up to 65 times fewer computing resources than the single-agent design.
Second author Mahmud Omar highlighted the importance of transparency in medicine: "When a single agent handles everything, you can't trace where it went wrong. With the orchestrator, every step is logged... That kind of transparency isn't optional in medicine." He also noted that autonomous agent tools are increasingly being used by clinicians and patients.
Looking ahead, the researchers plan to test these coordinated AI systems directly in clinical settings with real-time patient data. Nadkarni said that hospitals face constant overlapping demands and emphasized that "the future of health care AI is not a single super-intelligent system but a coordinated team of focused agents that work together to scale safely, control costs, and support real clinical operations."
