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This paper is in the following e-collection/theme issue:

AI Language Models in Health Care (290) Clinical Informatics (1498) Clinical Information and Decision Making (2151) Tools, Programs and Algorithms (425) Decision Support for Health Professionals (1697) Methods and Instruments in Medical Informatics (227) Ontologies, Classifications, and Coding (358) Machine Learning (2311)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/82026, first published .
Infographic: MECR-RAG improves ED triage accuracy compared to LLMs, achieving expert agreement.

Multi-Evidence Clinical Reasoning With Retrieval-Augmented Generation for Emergency Triage: Retrospective Evaluation Study

Multi-Evidence Clinical Reasoning With Retrieval-Augmented Generation for Emergency Triage: Retrospective Evaluation Study

Authors of this article:

Hang Sheung Wong1 Author Orcid Image ;   Tsz Kwan Wong2 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Song M, Kim J, Jang E, Kwon S. Interpretable Machine Learning for Emergency Department Triage: Clinical Insights from 133,198 Patients Using the Korean Triage and Acuity Scale (KTAS). Diagnostics 2026;16(6):954 View
  2. Šoša I. A Two-Stage In Silico-Guided Workflow for Forensic Toxicology: Empirical Validation via Capillary Zone Electrophoresis Prior to Mass-Spectrometric Confirmation. Toxics 2026;14(5):451 View