Abstract
Objective: Being publicly available, easy to use, and continuously evolving, next-generation chatbots have the potential to be used in triage, one of the most critical functions of an Emergency Department. The aim of this study was to assess the performance of Generative Pre-trained Transformer 4 (GPT-4), Bard and Claude during decision-making for Emergency Department triage.
 Material and Methods: This was a preliminary cross-sectional study conducted with 50 case scenarios. Emergency Medicine specialists determined the reference Emergency Severity Index triage category of each scenario. Subsequently, each case scenario was queried using three chatbots. Inconsistent classifications between the chatbots and references were defined as over-triage (false positive) or under-triage (false negative). The primary and secondary outcomes were the predictive performance of chatbots and the difference between them in predicting high acuity triage.
 Results: F1 Scores for GPT-4, Bard, and Claude for predicting Emergency Severity Index 1 and 2 were 0.899, 0.791, and 0.865 respectively. The ROC Curve of GPT-4 for high acuity predictions showed an area under the curve (AUC) of 0.911 (95% CI: 0,814-1; p
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