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Large language models (LLMs) like ChatGPT offer health information but need improved readability and linguistic equity for diverse populations. Ensuring default responses are accurate and understandable is crucial for informed health decisions.

Keywords:
artificial intelligenceconversational agentshealth equityhealth informationhealth literacylanguage equitylarge language modelsmultilingualismonline health informationpublic healthvaccination

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Area of Science:

  • Digital Health
  • Health Informatics
  • Public Health

Background:

  • Large language models (LLMs) are increasingly used for health information, presenting opportunities to overcome traditional access barriers.
  • However, the quality of LLM-generated health information is inconsistent, with limited research on non-English language responses.
  • This study focuses on health equity by evaluating vaccination information quality across languages.

Purpose of the Study:

  • To assess the quality and language equity of vaccination information from ChatGPT and the Centers for Disease Control and Prevention (CDC) in English and Spanish.
  • To highlight the necessity of cross-language evaluations for equitable health information access.

Main Methods:

  • Comparative analysis of ChatGPT and CDC responses to vaccination queries in English and Spanish.
  • Quantitative and qualitative assessments of accuracy, readability (Flesch-Kincaid grade level), and understandability (NIH PEMAT).

Main Results:

  • Both ChatGPT and CDC provided highly accurate and understandable responses (scores >95%).
  • Readability scores, particularly in English, often exceeded recommended levels (e.g., ChatGPT English avg. grade level 12.84 vs. recommended 6).
  • CDC responses showed better readability than ChatGPT; some Spanish responses had unnatural phrasing due to direct translation.

Conclusions:

  • LLMs like ChatGPT show promise for health information but require enhancements in readability and linguistic equity.
  • Default LLM interactions, often by users without advanced skills, significantly influence health perceptions.
  • Ensuring default responses are accurate, understandable, and equitable is vital for public health and informed decision-making across communities.