AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025450097
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PERSPECTIVE ARTICLE

Use of natural language processing in the emergency department: A clinical overview on the state of the art

Carly Hudson1,2,3* ,  Adrian Goldsworthy2,4,5,6 ,  Thuy Linh Phan2,7 ,  Anu Joy2 ,  Oystein Tronstad4,8 ,  Marcus Randall2
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1 Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Queensland, Australia
2 Bond Business School, Bond University, Gold Coast, Queensland, Australia
3 Faculty of Medicine and Health, University of New England, Armidale, New South Wales, Australia
4 Critical Care Research Group, The Prince Charles Hospital, Brisbane, Queensland, Australia
5 Harry Butler Institute, Murdoch University, Perth, Western Australia, Australia
6 Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
7 University of Queensland Business School, University of Queensland, Brisbane, Queensland, Australia
8 Department of Physiotherapy, The Prince Charles Hospital, Brisbane, Queensland, Australia
AIH, 025450097 https://doi.org/10.36922/AIH025450097
Received: 5 November 2025 | Revised: 4 January 2026 | Accepted: 21 January 2026 | Published online: 5 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Machine learning (ML) and artificial intelligence are increasingly ubiquitous in healthcare data analytics. To date, however, ML has been largely restricted to the analysis of structured data. While natural language processing (NLP) is gaining prominence in healthcare, substantial challenges remain in the generation and analysis of unstructured data. Emergency departments, which are increasingly under-resourced and overburdened, may benefit from the implementation of ML techniques that incorporate NLP to support clinical decision-making and improve patient care. Historically, regional and cultural variations have posed significant challenges to the widespread application of ML algorithms beyond their original training datasets. The rapidly increasing use of NLP within clinical note-taking applications provides avenues to assist in standardizing unstructured data and extracting meaningful insights to improve generalization and clinical translation.

Graphical abstract
Keywords
Natural language processing
Emergency department
Healthcare data
Unstructured data
Triage text
Funding
This research was funded by the Queensland Mental Health Commission.
Conflict of interest
The authors declare that they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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