A multi-agent large language model framework to automatically assess performance of a clinical AI Triage tool

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.