Orchestrated multi agents sustain accuracy under clinical-scale workloads compared to a single agent
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View abstract on PubMed
Summary
This summary is machine-generated.Orchestrated multi-agent systems using large language models (LLMs) maintain accuracy and efficiency for clinical workloads, outperforming single-agent approaches under heavy task loads.
Area Of Science
- Artificial Intelligence
- Clinical Informatics
- Computational Medicine
Background
- Large language models (LLMs) show promise for clinical applications but require evaluation under realistic workloads.
- Scalability and performance of LLM agent architectures in healthcare settings are not well-understood.
- Heterogeneous clinical tasks present unique challenges for single-agent LLM systems.
Purpose Of The Study
- To compare the performance of single-agent versus multi-agent LLM systems for clinical-scale workloads.
- To evaluate LLM accuracy, efficiency, and latency under varying batch sizes simulating clinical traffic.
- To assess the impact of task isolation on LLM performance in a multi-agent framework.
Main Methods
- Two LLM configurations were tested: a single agent and an orchestrated multi-agent system.
- Tasks included data retrieval, extraction, and dosing calculations, with batch sizes ranging from 5 to 80.
- Performance metrics included pooled accuracy, token usage, and latency, analyzed across four LLM checkpoints.
Main Results
- Multi-agent systems maintained high accuracy under load (90.6% at 5 tasks, 65.3% at 80 tasks), while single-agent accuracy declined sharply (73.1% to 16.6%).
- Significant performance differences emerged beyond 10 tasks (FDR-adjusted p < 0.01).
- Multi-agent execution reduced token usage up to 65-fold and limited latency growth compared to single-agent runs.
Conclusions
- Lightweight orchestration of LLM agents is effective for clinical-scale workloads.
- Multi-agent systems offer superior accuracy, efficiency, and auditability compared to single-agent LLMs in healthcare.
- Task isolation in multi-agent designs prevents context interference and sustains performance.
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