Development of BERT-based large language models for emergency department triage using real-world conversations

BERT-based LLMs trained on real-world ED conversations significantly outperform general-purpose models like ChatGPT in triage accuracy. This approach demonstrates the potential for enhancing clinical decision support with interpretable and efficient AI.

Authors

Jong-Hak Park (ORCID: https://orcid.org/0000-0001-9481-6358)
Sukyo Lee (ORCID: https://orcid.org/0000-0002-7105-1951)
Sejoong Ahn
Sungwoo Moon (ORCID: https://orcid.org/0000-0003-3947-2673)
Sumin Jung (ORCID: https://orcid.org/0009-0009-7663-170X)
Hanjin Cho

Institutions

Ansan University (KR)

Publication Details

Journal: Journal of the American Medical Informatics Association
Published: 2026-01-14
DOI: https://doi.org/10.1093/jamia/ocag007
Primary Topic: Artificial Intelligence in Healthcare and Education
Type: article
Field-Weighted Citation Impact: 0.00

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article

Development of BERT-based large language models for emergency department triage using real-world conversations

Jong-Hak Park, Sukyo Lee, Sejoong Ahn, Sungwoo Moon et al.

Jan 14, 2026

Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and Education

article

Development of BERT-based large language models for emergency department triage using real-world conversations

Jong-Hak Park, Sukyo Lee, Sejoong Ahn, Sungwoo Moon, Sumin Jung, Hanjin Cho

January 14, 2026article en

Abstract

BERT-based LLMs trained on real-world ED conversations significantly outperform general-purpose models like ChatGPT in triage accuracy. This approach demonstrates the potential for enhancing clinical decision support with interpretable and efficient AI.

Journal of the American Medical Informatics Association

Ansan University (KR)

Openalex Percentile: Top 67%

Artificial Intelligence in Healthcare and Education

DOI Source OpenAlex

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