ArticleFrontiers in pediatrics2026
Should we leave paediatric emergency triage to artificial intelligence? A comparison of ChatGPT 4o and Grok 3.
Article in Frontiers in pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Evaluating Triage Acuity and Injury Mechanisms as Drivers of Emergency Department Length of Stay in Pediatric Forensic Cases.Journal of clinical medicine · 2026Article
- Are artificial intelligence systems ready for pediatric surgical decision-making? A comparative evaluation of large language models versus pediatric surgeons.Pediatric surgery international · 2026Article
- Freely accessible large language models for parent education in pediatric immune thrombocytopenia: an expert-rated cross-sectional study of safety, readability, and guideline concordance.Frontiers in pediatrics · 2026Article
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3 authors.
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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: A total of 1,505 paediatric emergency patients were included in the analysis. No ESI-1 cases were observed; therefore, critical patients were defined as ESI-2. Nurses achieved 53.1% (95% CI: 50.6-55.6) accuracy in triage assessments, while ChatGPT 4o achieved 76.1% (95% CI: 73.9-78.2) and Grok 3 achieved 47.0% (95% CI: 44.5-49.6) accuracy (Cochran's Q = 275.68, 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.
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