Readability and Health Literacy Scores for ChatGPT-Generated Dermatology Public Education Materials: Cross-Sectional Analysis of Sunscreen and Melanoma Questions

Katie Roster; Rebecca B Kann; Banu Farabi; Christian Gronbeck; Nicholas Brownstone; Shari R Lipner

doi:10.2196/50163

Readability and Health Literacy Scores for ChatGPT-Generated Dermatology Public Education Materials: Cross-Sectional Analysis of Sunscreen and Melanoma Questions

JMIR Dermatol. 2024 Mar 6:7:e50163. doi: 10.2196/50163.

Authors

Katie Roster¹, Rebecca B Kann¹, Banu Farabi², Christian Gronbeck³, Nicholas Brownstone⁴, Shari R Lipner⁵

Affiliations

¹ New York Medical College, New York, NY, United States.
² Dermatology Department, NYC Health + Hospital/Metropolitan, New York, NY, United States.
³ Department of Dermatology, University of Connecticut HealthCenter, Framington, CT, United States.
⁴ Department of Dermatology, Temple University Hospital, Philadelphia, PA, United States.
⁵ Department of Dermatology, Weill Cornell Medicine, New York, NY, United States.

PMID: 38446502
PMCID: PMC10955394
DOI: 10.2196/50163

No abstract available

Keywords: AI; ChatGPT; LLM; LLMs; NLP; artificial intelligence; comprehensibility; comprehensible; dermatology; disparities; disparity; generative; health disparities; health education; health information; health literacy; language model; language models; large language model; natural language processing; online information; patient education; public education; readability; understandability.