Large Language Models Show Comparable Response Performance but Vary in Readability Regarding Patient Questions on Hip Arthroscopy.

ABSTRACT:

Purpose

To compare the quality of large language model (LLM) responses to frequently asked questions regarding hip arthroscopy, assess the incorrect response rate of LLMs, and compare the readability among different LLM outputs.

Methods

Three LLMs, including OpenAI Chat Generative Pre-Trained Transformer (ChatGPT) 3.5, Microsoft Co-Pilot, and Google Gemini, were each queried with 10 frequently asked questions regarding hip arthroscopy. Two high-volume hip arthroscopists graded the responses on a 4-point Likert scale (1 = excellent, requiring no clarification; 2 = satisfactory, requiring minimal clarification; 3 = satisfactory, requiring moderate clarification; and 4 = unsatisfactory, requiring substantial clarification). Additionally, the 2 graders ranked the responses from

SUBMITTER: Mun JS 

PROVIDER: S-EPMC12827216 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature