Generative Artificial Intelligence Methodology Reporting in Otolaryngology: A Scoping Review.

ABSTRACT:

Objective

Researchers in otolaryngology-head and neck surgery (OHNS) have sought to explore the potential of large language models (LLMs), but many publications do not include crucial information, such as prompting approach and model parameters. This has substantial implications for reproducibility, since LLMs can generate different output based on differences in "prompt engineering." We aimed to critically review methodological reporting and quality of LLM-focused literature in OHNS.

Data sources

Databases were searched in October 2024, including PubMed, Embase, Web of Science, ISCA Archive, IEEE Xplore, arXiv, medRxiv, and engRxiv.

Review methods

Abstract and full text review, as well as data extraction, were performed by two independent reviewers. All primary studi

SUBMITTER: Alter IL 

PROVIDER: S-EPMC12720411 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature