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

Journal: Pediatric emergency care

PMID: 39761573

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.

Authors

Brandon Ho
Meng Lu

Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, United States; Department of Mechanical Engineering, Iowa State University, Ames, IA 500110, United States. Electronic address: menglu@iastate.edu.
Xuan Wang

Baylor Scott & White Health, Dallas, TX, USA.
Russell Butler

University of California Davis School of Medicine, Sacramento, CA.
Joshua Park
Dennis Ren

Keywords

Artificial Intelligence Child Emergency Service, Hospital Female Generative Artificial Intelligence Humans Male Reproducibility of Results Sensitivity and Specificity Severity of Illness Index Triage

External Resources

View on PubMed Access via DOI PubMed (39761573)

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

Abstract

Authors

Keywords

External Resources

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