ChatGPT’s Readability Gap in Opioid Use Disorder Education

A recent abstract describes a comparison of ChatGPT-generated responses with opioid use disorder (OUD) frequently asked questions (FAQs) from U.S. health organizations, focusing on readability, linguistic complexity, and stigmatizing language in patient-facing materials.

In that summary, questions about understandability and stigma are examined in the context of generative AI use for patient communication. Readability and non-stigmatizing language are presented as central health-literacy considerations in a stigmatized and literacy-sensitive domain such as opioid use disorder.

The materials under comparison are ChatGPT (GPT-4o)–generated responses and 50 paired U.S. health organization FAQs about opioid use disorder (OUD). The abstract evaluates word and sentence counts, lexical density, multiple validated readability indices, and stigmatizing term frequency. The focus remains on measurable features of written patient education materials rather than on downstream clinical or behavioral outcomes.

The analysis found that ChatGPT responses were substantially longer and more linguistically complex than the FAQs. ChatGPT answers had higher word and sentence counts, greater lexical density, and higher grade-level readability scores across six indices (Coleman-Liau, Gunning Fog, SMOG, Flesch-Kincaid, Automated Readability Index, and Flesch Reading Ease), all statistically significant. In contrast, the frequency of stigmatizing terms was similar between ChatGPT responses and FAQs.

Readability and linguistic complexity are treated as practical health-literacy barriers in AI-generated patient education, particularly in a stigmatized condition such as OUD. Although ChatGPT responses were described as more comprehensive, their increased length and complexity may limit accessibility. The findings highlight a trade-off between comprehensiveness and readability and emphasize the importance of plain-language prompting and human review when using large language models for patient-facing education.

Key Takeaways:

• The analysis compared 50 ChatGPT (GPT-4o)–generated responses with U.S. health organization OUD FAQs, evaluating readability, linguistic complexity, and stigmatizing language.
• ChatGPT responses were longer and more complex, with significantly higher grade-level readability scores, while stigmatizing term frequency was similar between sources.
• The findings underscore the need for plain-language optimization and human oversight when using AI-generated content in a stigmatized and health-literacy–sensitive domain such as opioid use disorder education.