Large language models' performances regarding common patient questions about osteoarthritis: A comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Perplexity.

ABSTRACT:

Background

Large Language Models (LLMs) have gained much attention and, in part, have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses. Osteoarthritis (OA) is a common topic in skeletal muscle disorders, and patients often seek information about it online. Our study evaluated the ability of 3 LLMs (ChatGPT-3.5, ChatGPT-4.0, and Perplexity) to accurately answer common OA-related queries.

Methods

We defined 6 themes (pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis) based on a generalization of 25 frequently asked questions about OA. Three consultant-level orthopedic specialists independently rated the LLMs' replies on a 4-point accuracy scale. The fin

SUBMITTER: Cao M 

PROVIDER: S-EPMC12268069 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature