Readability of Artificial-Intelligence-Generated Responses to Patient Questions About Hearing Loss and Dementia

Objective: This study examines the readability of artificial intelligence (AI)-generated responses to public queries regarding hearing loss (HL), dementia, and the link between them. Methods: This cross-sectional study submitted 20 standardized patient-facing questions about HL, dementia, and their relationship, to three AI platforms (ChatGPT, Claude, and Perplexity AI). At the time of data collection, the default models were, respectively, GPT-5.3 Instant, Claude Sonnet 4.6, and Perplexity AI default “best” model selection. All 60 responses were assessed for readability using two validated metrics, Flesch–Kincaid Grade Level (FKGL) and Simple Measure of Gobbledygook (SMOG). The American Medical Association (AMA)/National Institutes of Health (NIH)-recommends eighth-grade reading level for patient education materials. Descriptive statistics were calculated for readability scores across platforms and 10 topic domains (SPSS 31.0). Results: Across the three AI platforms queried, FKGL median readability for responses to hearing-related questions was at the upper high school to junior college grade level (12.3–15.3) and 1–2 grade levels lower with SMOG (11.4–13.0). For dementia responses, the readability was at 15.2–16.3 grade level with FKGL and 12.9–14.1 using SMOG. No statistically significant differences were found between AI platforms overall and by domain. Claude mostly received high grade level scores for domains regarding symptoms and condition management, for both HL and dementia with FKGL and SMOG. Conclusion: AI chatbots are used by the public to seek health information, yet this study found that ChatGPT, Claude, and Perplexity AI produced responses to questions about HL and dementia at 3–8 grade levels above the AMA/NIH-recommended eighth-grade benchmark. These findings align with a growing literature showing that AI-generated health content routinely exceeds recommended readability thresholds across many medical specialties. This is particularly concerning for the aging populations most affected by HL and dementia. The authors urge relevant professional organizations to audit their public-facing materials for readability compliance.