Impact of Demographic Modifiers on Readability of Myopia Education Materials Generated by Large Language Models

Patient demographic information impacts the reading level of educational material generated by Gemini but not by ChatGPT or Copilot. As patients use LLMs to understand ophthalmologic diagnoses like myopia, clinicians and users should be aware of demographic influences on readability. Patient gender, race, and ethnicity may be overlooked variables affecting the readability of LLM-generated education materials, which can impact patient care. Future research could focus on the accuracy of generated information to identify potential risks of misinformation.