Accessibility settings

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

AI Language Models in Health Care (715) Clinical Information and Decision Making (3611) Natural Language Processing (1251) Decision Support for Health Professionals (2173) Artificial Intelligence (4634) Generative Language Models Including ChatGPT (1457)

Published on in Vol 14 (2026)

This is a member publication of Lifespan

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/83318, first published .
Medical team attending to patient in hospital bed with oxygen mask.

Artificial Intelligence Models for Predicting Triage in Emergency Departments: Seven-Month Retrospective Comparative Study of Natural Language Processing, Large Language Model, and Joint Embedding Predictive Architectures

Artificial Intelligence Models for Predicting Triage in Emergency Departments: Seven-Month Retrospective Comparative Study of Natural Language Processing, Large Language Model, and Joint Embedding Predictive Architectures

Authors of this article:

Edouard Lansiaux1 Author Orcid Image ;   Ramy Azzouz2, 3 Author Orcid Image ;   Emmanuel Chazard3, 4 Author Orcid Image ;   Amélie Vromant5 Author Orcid Image ;   Eric Wiel1, 3 Author Orcid Image
Article Authors Cited by (6) Tweetations (6) Metrics

Journals

  1. Chen L, Wang Q, Mao X, Xia S, Ren A, Yin L, Wang H, Liu N. Role reconstruction and competency requirements of emergency nurses in the human-machine collaborative mode: a qualitative study. Frontiers in Public Health 2026;14 View
  2. Song Y, Luan X, Chen X. Establishing AI-driven evaluation standards and human-in-the-loop guidance for digital first aid education: a perspective. Frontiers in Public Health 2026;14 View
  3. Lee E, Jung J, Kim D, Park J, Kwak Y, Kuo Y. Performance and safety of a fine-tuned small language model for pediatric emergency triage: A benchmark study. PLOS One 2026;21(6):e0350770 View
  4. Soleimani Sardou S, Rezvaninejad R, Rezvaninejad F, Nekouei A. Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models. BMC Cancer 2026;26(1) View
  5. Broniszewska P, Kita N, Świech J, Apanasewicz K, Kopacki F, Szczeblewska W, Jaskot D, Marzec J, Sztenc N, Kamrowska M. ARTIFICIAL INTELLIGENCE-BASED TRIAGE IN EMERGENCY DEPARTMENTS: PERFORMANCE, WORKFLOW OPTIMIZATION, AND IMPLEMENTATION CHALLENGES. International Journal of Innovative Technologies in Social Science 2026;4(2(50)) View
  6. Pamplin J. The Edge Advantage: Why Computational Efficiency Matters for Artificial Intelligence in Military Medicine. Military Medicine 2026 View