xRead - Incorporating Artificial Intelligence into Clinical Practice (March 2026)

The Laryngoscope

SCOPING REVIEW OPEN ACCESS

Applications of Natural Language Processing in Otolaryngology: A Scoping Review Norbert Banyi 1 | Brian Ma 2 | Ameen Amanian 3 | Andrés Bur 4 | Arman Abdalkhani 3

1 The University of British Columbia, Faculty of Medicine, Vancouver, Canada | 2 Department of Cellular & Physiological Sciences, University of British Columbia, Vancouver, Canada | 3 Division of Otolaryngology—Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada | 4 Department of Otolaryngology—Head and Neck Surgery, University of Kansas Medical Centre, Kansas City, Kansas, USA Correspondence: Arman Abdalkhani (dr.a@ubc.ca) Received: 5 August 2024 | Revised: 17 February 2025 | Accepted: 14 March 2025 Funding: This work was supported by the Kansas Institute of Precision Medicine, P20GM130423. Keywords: artificial intelligence | big data | chatbots | ChatGPT | data mining | generative AI | large language models | medical education | natural language processing | otolaryngology ABSTRACT Objective: To review the current literature on the applications of natural language processing (NLP) within the field of otolaryngology. Data Sources: MEDLINE, EMBASE, SCOPUS, Cochrane Library, Web of Science, and CINAHL. Methods: The preferred reporting Items for systematic reviews and meta-analyzes extension for scoping reviews checklist was followed. Databases were searched from the date of inception up to Dec 26, 2023. Original articles on the application of language-­ based models to otolaryngology patient care and research, regardless of publication date, were included. The studies were classi fied under the 2011 Oxford CEBM levels of evidence. Results: One-hundred sixty-six papers with a median publication year of 2024 (range 1982, 2024) were included. Sixty-one percent (102/166) of studies used ChatGPT and were published in 2023 or 2024. Sixty studies used NLP for clinical education and decision support, 42 for patient education, 14 for electronic medical record improvement, 5 for triaging, 4 for trainee education, 4 for patient monitoring, 3 for telemedicine, and 1 for medical translation. For research, 37 studies used NLP for extraction, classification, or analysis of data, 17 for thematic analysis, 5 for evaluating scientific reporting, and 4 for manu script preparation. Conclusion: The role of NLP in otolaryngology is evolving, with ChatGPT passing OHNS board simulations, though its clinical application requires improvement. NLP shows potential in patient education and post-treatment monitoring. NLP is effective at extracting data from unstructured or large data sets. There is limited research on NLP in trainee education and administrative tasks. Guidelines for NLP use in research are critical.

1 | Introduction Artificial Intelligence (AI) has grown rapidly in healthcare with applications ranging from diagnostics, prognostics, to treatment planning [1–3]. Amidst the broad spectrum of AI applications, Natural Language Processing (NLP) has been relatively uncharted

in healthcare until recently. NLP is a specialized branch of AI that focuses on understanding, interpreting, and generating human language in an intelligent and meaningful way. Large language models (LLMs) are novel algorithms based on the transformer architecture proposed in 2017 that enable

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2025 The Author(s). The Laryngoscope published by Wiley Periodicals LLC on behalf of The American Laryngological, Rhinological and Otological Society, Inc.

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The Laryngoscope, 2025; 135:3049–3063 https://doi.org/10.1002/lary.32198