Should we leave paediatric emergency triage to artificial intelligence? A comparison of ChatGPT 4o and Grok 3

Abstract

Background: The growing number of patients in paediatric emergency departments requires fast and precise triage assessments. The implementation of large language models faces obstacles due to their limited interpretability. We aimed to compare the performance of ChatGPT 4o and Grok 3 with that of nurses and physicians in paediatric emergency triage. Methods: This prospective observational study evaluated paediatric emergency patients presenting to our paediatric emergency department between March and April 2025. Demographic data, chronic disease status, presenting complaints, and vital signs were documented. Patients were triaged according to ESI criteria by nurses, paediatric specialists (gold standard), ChatGPT 4o, and Grok 3. Inter-rater agreement was analysed using Cohen's kappa**. Cochran's Q and McNemar's tests were used for paired comparisons.*. Results: < 0.01) for children with chronic illnesses. Conclusion: ChatGPT 4o achieved the most favourable balance of sensitivity and specificity. The superior performance of nurses in recognising critically ill patients with chronic diseases suggests that AI systems should augment nursing expertise rather than replace it.

Ähnliche Arbeiten