Artificial Intelligence Models for Predicting Triage in Emergency Departments: Seven-Month Retrospective Comparative Study of Natural Language Processing, Large Language Model, and Joint Embedding Predictive Architectures

The LLM-based architecture (URGENTIAPARSE) demonstrated the highest accuracy for ED triage prediction among the tested models, outperforming traditional NLP, JEPA, and current nurse triage practices. However, severe overfitting, extreme selection bias (657/73,236, 0.90%, inclusion), a monocentric design, and sparse high-acuity representation limit clinical applicability. Before deployment, the model requires regularization, external validation across diverse EDs, prospective testing, and comprehensive safety evaluation, particularly for undertriage detection. Integration of AI triage support systems shows promise but demands rigorous validation, bias mitigation, and transparent uncertainty quantification to ensure patient safety.