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

Journal: Clinical ophthalmology (Auckland, N.Z.)
Published Date: Dec 4, 2024

Abstract

BACKGROUND: The rise of large language models (LLM) promises to widely impact healthcare providers and patients alike. As these tools reflect the biases of currently available data on the internet, there is a risk that increasing LLM use will proliferate these biases and affect information quality. This study aims to characterize the effects of different race, ethnicity, and gender modifiers in question prompts presented to three large language models (LLM) on the length and readability of patient education materials about myopia.

Authors

  • Gabriela G Lee
    Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
  • Deniz Goodman
    Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
  • Ta Chen Peter Chang
    Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.

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