Applications of Natural Language Processing in Otolaryngology: A Scoping Review

To review the current literature on the applications of natural language processing (NLP) within the field of otolaryngology.

MEDLINE, EMBASE, SCOPUS, Cochrane Library, Web of Science, and CINAHL.

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 classified under the 2011 Oxford CEBM levels of evidence.

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 manuscript preparation.

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.