ArticlePEC innovation2025
Leveraging artificial intelligence chatbots for anemia prevention: A comparative study of ChatGPT-3.5, copilot, and Gemini outputs against Google Search results.
Article in PEC innovation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Quality and Readability Analysis of Artificial Intelligence-Generated Medical Information Related to Prostate Cancer: A Cross-Sectional Study of ChatGPT and DeepSeek.The world journal of men's health · 2026Article
- Artificial intelligence in public health-challenges and opportunities.European journal of clinical nutrition · 2026Review
- Evaluation of Arabic-Language AI Chatbot Responses to Migraine-Related Questions: A Comparative Cross-Sectional Study.Journal of clinical medicine · 2026Article
- Quality assessment of patient-facing urologic telesurgery content using validated tools.Journal of robotic surgery · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: This study evaluated the understandability, actionability, and readability of text on anemia generated by artificial intelligence (AI) chatbots. Methods: This cross-sectional study compared texts generated by ChatGPT-3.5, Microsoft Copilot, and Google Gemini at three levels: "normal," "6th grade," and "PEMAT-P version." Additionally, texts retrieved from the top eight Google Search results for relevant keywords were included for comparison. All texts were written in Japanese. The Japanese version of the PEMAT-P was used to assess understandability and actionability, while jReadability was used for readability. A systematic comparison was conducted to identify the strengths and weaknesses of each source. Results: Texts generated by Gemini at the 6th-grade level ( Innovation: This study is the first to objectively and quantitatively evaluate the understandability and actionability of educational materials on anemia prevention. By utilizing PEMAT-P and jReadability, the study demonstrated the superiority of Gemini in terms of understandability and readability through measurable data. This innovative approach highlights the potential of AI chatbots as a novel method for providing public health information and addressing health disparities. Conclusion: AI-generated texts on anemia were found to be more readable and easier to understand than traditional web-based texts, with Gemini demonstrating the highest level of understandability. Moving forward, improvements in prompts will be necessary to enhance the integration of visual elements that encourage actionable responses in AI chatbots.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.