Articlenpj health systems2026
Orchestrated multi agents sustain accuracy under clinical-scale workloads compared to a single agent.
Article in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.medRxiv : the preprint server for health sciences · 2026Article
- A unified multimodal GenAI platform integrating GraphRAG multi-agent systems and custom language models for intelligent document processing and knowledge synthesis.Scientific reports · 2026Article
- Automated extraction and optimization of protein purification protocols using multi-agent large language models.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
- Update of
Authors and funding
11 authors.
Funding
Abstract
We tested state-of-the-art LLMs under clinical-scale workloads using two designs: a single agent handling all tasks and a multi-agent orchestrator assigning each task to a dedicated worker. Across retrieval, extraction, and dosing tasks, batch sizes ranged from 5-80. Multi-agent accuracy remained high (90.6% at 5 tasks; 65.3% at 80), while single-agent accuracy collapsed (73.1% to 16.6%; p < 0.01). Multi-agent runs used up to 65-fold fewer tokens and limited latency growth. These findings show that lightweight orchestration preserves accuracy and efficiency under mixed-task clinical loads.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.