Evaluation of Generative Artificial Intelligence Models in Predicting Pediatric Emergency Severity Index Levels

Abstract

OBJECTIVE: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric emergency department patients and assess the impact of medically oriented fine-tuning. METHODS: Seventy pediatric clinical vignettes from the ESI Handbook version 4 were used as the gold standard. Each AI model predicted the ESI level for each vignette. Performance metrics, including sensitivity, specificity, and F1 score, were calculated. Reliability was assessed by repeating the tests and measuring the interrater reliability using Fleiss kappa. Paired t tests were used to compare the models before and after fine-tuning. RESULTS: Claude-3 Opus achieved the highest performance amongst the untrained models with a sensitivity of 80.6% (95% confidence interval [CI]: 63.6-90.7), specificity of 91.3% (95% CI: 83.8-99), and an F1 score of 73.9% (95% CI: 58.9-90.7). After fine-tuning, the GPT-4.0 model showed statistically significant improvement with a sensitivity of 77.1% (95% CI: 60.1-86.5), specificity of 92.5% (95% CI: 89.5-97.4), and an F1 score of 74.6% (95% CI: 63.9-83.8, P < 0.04). Reliability analysis revealed high agreement for Claude-3 Opus (Fleiss κ: 0.85), followed by Mistral-Large (Fleiss κ: 0.79) and trained GPT-4.0 (Fleiss κ: 0.67). Training improved the reliability of GPT models ( P < 0.001). CONCLUSIONS: Generative AI models demonstrate promising accuracy in predicting pediatric ESI levels, with fine-tuning significantly enhancing their performance and reliability. These findings suggest that AI could serve as a valuable tool in pediatric triage.

Ähnliche Arbeiten