Abstract
BACKGROUND: Mpox (monkeypox) outbreaks since 2022 have emphasized the importance of accessible health education materials. However, many Japanese online resources on mpox are difficult to understand, creating barriers for public health communication. Recent advances in artificial intelligence (AI) such as ChatGPT-4o show promise in generating more comprehensible and actionable health education content. OBJECTIVE: The aim of this study was to evaluate the comprehensibility, actionability, and readability of Japanese health education materials on mpox compared with texts generated by ChatGPT-4o. METHODS: A cross-sectional study was conducted using systematic quantitative content analysis. A total of 119 publicly available Japanese health education materials on mpox were compared with 30 texts generated by ChatGPT-4o. Websites containing videos, social media posts, academic papers, and non-Japanese language content were excluded. For generating ChatGPT-4o texts, we used 3 separate prompts with 3 different keywords. For each keyword, text generation was repeated 10 times, with prompt history deleted each time to prevent previous outputs from influencing subsequent generations and to account for output variability. The Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) was used to assess the understandability and actionability of the generated text, while the Japanese Readability Measurement System (jReadability) was used to evaluate readability. The Journal of the American Medical Association benchmark criteria were applied to evaluate the quality of the materials. RESULTS: A total of 119 Japanese mpox-related health education web pages and 30 ChatGPT-4o-generated texts were analyzed. AI-generated texts significantly outperformed web pages in understandability, with 80% (24/30) scoring ≥70% in PEMAT-P (P<.001). Readability scores for AI texts (mean 2.9, SD 0.4) were also higher than those for web pages (mean 2.4, SD 1.0; P=.009). However, web pages included more visual aids and actionable guidance such as practical instructions, which were largely absent in AI-generated content. Government agencies authored 90 (75.6%) out of 119 web pages, but only 31 (26.1%) included proper attribution. Most web pages (117/119, 98.3%) disclosed sponsorship and ownership. CONCLUSIONS: AI-generated texts were easier to understand and read than traditional web-based materials. However, web-based texts provided more visual aids and practical guidance. Combining AI-generated texts with traditional web-based materials may enhance the effectiveness of health education materials and improve accessibility to a broader audience. Further research is needed to explore the integration of AI-generated content into public health communication strategies and policies to optimize information delivery during health crises such as the mpox outbreak.