ArticleBMC nephrology2026
Guidelines vs. generative AI in CKD patient education: the role of prompt engineering and expert blinded evaluation.
Article in BMC nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to evaluate the accuracy, content quality, and readability of patient education responses related to chronic kidney disease (CKD) generated by large language models (ChatGPT-4o mini and Gemini) compared to guideline group. Fifteen frequently asked CKD-related questions were selected using global Google Trends data and posed to both AI models and guideline-based sources. Responses were anonymized and evaluated by four independent nephrology professors using the CLEAR Tool, assessing completeness, appropriateness, evidence basis, and clarity. Both AI models significantly outperformed guideline responses across all CLEAR Tool domains (p < 0.001), with ChatGPT-4o mini achieving the highest median score (21.0 [IQR: 5.0] vs. Gemini: 17.0 [IQR: 5.0], Guideline: 13.0 [IQR: 2.0]). Initial readability analysis showed that guideline responses were easier to comprehend (Flesch-Kincaid Grade Level (FKGL): 9.40; Flesch Reading Ease (FRE): 52.01) than AI-generated content (ChatGPT FKGL: 11.34, FRE: 36.17; Gemini FKGL: 9.62, FRE: 46.36). However, when a structured prompt was applied, AI responses demonstrated significant improvements in readability, reducing the required literacy level to approximately the 7th-grade (ChatGPT FKGL: 7.87, FRE: 64.23; Gemini FKGL: 7.13, FRE: 61.45). These findings highlight the potential of prompt-guided AI models to generate accurate, accessible educational content for CKD. Prompt engineering emerges as a practical tool to enhance clarity and usability, particularly for populations with limited health literacy. Integration with frameworks like Retrieval-Augmented Generation may further improve reliability and safety in digital health communication.
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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.