arXiv:2605.24699v1 Announce Type: cross Abstract: Most reported gains on agentic-LLM clinical benchmarks are often attributed to prompt engineering, yet our results suggest that larger improvements can come from architectural and engine-level design. We present MDIA, a Multi-agent Diagnostic Intelligence Agent implemented as a 7-node specialty-routed clinical reasoning graph, on the full HealthBench Professional benchmark (n = 525), on a non-fine-tuned LLM. MDIA achieves 0.6272 under OpenAI's GPT-5.4-2026-03-05, which is +3.72 pp above the performance of OpenAI's ChatGPT for Clinicians. The ex
The rapid advancement in LLM capabilities and agentic architectures is enabling new approaches to complex problem-solving, such as clinical diagnostics, pushing beyond simple prompt engineering.
This breakthrough demonstrates the potential for AI agents to achieve significant performance gains in highly specialized fields, portending a future where complex white-collar tasks are increasingly automated by sophisticated multi-agent systems.
The focus for improving LLM performance shifts from mere prompt engineering to architectural and engine-level design of multi-agent systems, signifying a maturation in agentic AI development.
- · AI Agent developers
- · Healthcare AI companies
- · LLM providers with advanced models
- · Patients accessing improved diagnostics
- · Traditional clinical decision support systems
- · AI platforms relying solely on prompt engineering
- · Healthcare professionals resistant to AI integration
Multi-agent systems will become the dominant paradigm for complex AI applications like diagnostics, challenging single-model approaches.
This improved diagnostic accuracy could reduce misdiagnoses and accelerate treatment pathways, leading to broader adoption of AI in clinical settings.
The success of multi-agent architectures in healthcare could catalyze their development across numerous other professional domains, fundamentally reshaping knowledge work.
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