Articlenpj health systems2026
A multi-agent large language model framework to automatically assess performance of a clinical AI Triage tool.
Article in npj health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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.
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Authors and funding
9 authors.
Funding
Abstract
Radiology reports can be used as a surrogate for performance of clinical AI tools. Radiology reports were analyzed by an ensemble of eight open-source LLM models and a internal version of GPT-4o using a single multi-shot prompt that assessed for presence of ICH. Performance of the open-source models, consensus of models and GPT-4o were compared to human report review. Three ideal consensus LLM ensembles were tested for rating the performance of the triage tool. The capability of each LLM varied. The highest AUC performance was achieved with llama3.3:70b and GPT-4o. Using MCC the ideal combination of LLMs were: Full-9 Ensemble, Top-3 Ensemble and consensus. No statistically significant differences were observed between Top-3, Full-9, and consensus. An ensemble of open-source LLMs provides a more consistent and reliable method to derive a ground truth retrospective evaluation of a clinical AI triage tool over a single LLM alone.
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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.