Leveraging large language models to improve patient education on dry eye disease.

Background/objectives: Dry eye disease (DED) is an exceedingly common diagnosis in patients, yet recent analyses have demonstrated patient education materials (PEMs) on DED to be of low quality and readability. Our study evaluated the utility and performance of three large language models (LLMs) in enhancing and generating new patient education materials (PEMs) on dry eye disease (DED).

Subjects/methods: We evaluated PEMs generated by ChatGPT-3.

Quality, Reliability, Readability, and Accountability of Online Information on Leukocoria.

Purpose: To evaluate the quality, reliability, and readability of online patient educational materials on leukocoria.

Methods: In this cross-sectional study, the Google search engine was searched for the terms "leukocoria" and "white pupil." The first 50 search outcomes were evaluated for each search term based on predefined inclusion criteria, excluding duplicates, peer-reviewed papers, forum posts, paywalled content, and multimedia links.