Assessing Readability and DISCERN Quality of Osteoporosis Education Materials Generated by ChatGPT and Deepseek for Diverse Health Literacy Levels: A Cross-Sectional Study.

Background and Aims: Large language models (LLMs) are increasingly used for patient education, but their output quality across health literacy levels is unknown. This cross-sectional study compared ChatGPT-4o, ChatGPT-5, DeepSeek-V3.1, and DeepSeek-R2 in generating osteoporosis education materials tailored to low, moderate, and high health literacy. Methods: Six clinical domains were posed to each LLM, with prompts adapted for three literacy tiers per model (18 outputs per model). Outputs were aggregated into 12 composite texts (4 models × 3 tiers). Three blinded clinicians assessed information quality using DISCERN (0-80) and readability using Flesch-Kincaid (FKGL, lower = easier). Results: Mean DISCERN scores ranged 36-52/80 ("fair"); no output reached "excellent" (> 70/80). DeepSeek-V3.1 provided the highest treatment detail for high literacy and best low-literacy readability (FKGL 3.99). ChatGPT-5 performed most consistently across tiers. Median readability ranged from grade 4.8 to 10.2. All outputs lacked citations, publication dates, quantitative risk data, and uncertainty statements. Conclusion: LLMs can rapidly generate readable osteoporosis education, but current outputs require supplementation with references, risk statistics, and updated transparency before clinical use.