ArticleFrontiers in public health2026
Challenges of using generative AI for patient education in chronic heart failure: an evaluation of content quality, readability, and actionability in cross-platform LLM-generated texts.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Cross-platform evaluation of LLM-generated educational texts on cardiac myxoma: quality, readability, and actionability using network analysis and latent profile analysis.Frontiers in cardiovascular medicine · 2026Article
- Are AI tutors ready for the TCM classroom? A multimodal evaluation of large language models across cognitive levels in Chinese medicine education.Frontiers in medicine · 2026Article
- Challenges of using generative artificial intelligence for diabetes patient education: a cross-platform analysis of the quality, readability, and actionability of text generated by large language models.Frontiers in public health · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To compare the differences in content quality, readability, and actionability of patient education texts for self-management of chronic heart failure (CHF) generated by five mainstream large language models (LLMs) in China, and to provide a basis for platform selection and assessment framework construction for clinical use. Methods: A standardized set of 20 questions was developed based on literature review, guidelines, and consensus from cardiovascular experts, covering disease awareness, diagnosis and classification, treatment and rehabilitation, daily management and prevention, and psychosocial dimensions. Using a uniform prompt, responses were generated by DeepSeek-R1, Doubao, ERNIEBot 4.5 Turbo, Qwen3-Max-Thinking-Preview, and Kimi K2. The PEMAT-P scale was used to assess understandability and actionability, 36-item expanded EQIP (EQIP-36 score) scale was used to evaluate information completeness and standardization, and Global Quality Score (GQS) was used to assess overall quality. Additionally, seven readability formulas, including Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL), were computed for comparison. Results: Overall quality was high [GQS median 5.00 (4.00-5.00)] with significant between-platform differences (χ Conclusion: LLMs show potential for use in patient education for CHF, but there is a structural trade-off between information detail and readability, as well as gaps in actionability and verifiability. It is recommended to combine enhanced search and structured template generation strategies, and establish a governance feedback loop involving prompt engineering, clinical expert review, and continuous monitoring to improve readability alignment, completeness of action instructions, and patient safety.
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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 Socratic graph.