ArticleDigital health
The reliability and readability of large language models in answering patient questions on maintenance hemodialysis: A comparative study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Quality and Readability of Four AI Chatbots Answering Search-Derived Patient Questions About Primary Aldosteronism: A Cross-Sectional Comparative Study.Healthcare (Basel, Switzerland) · 2026Article
- Generative AI chatbots as sources of phimosis-related consumer health information: a cross-sectional comparison of quality, source transparency, and readability.Frontiers in public health · 2026Article
- Artificial Intelligence Chatbots as Sources of Cancer Pain Information: A Comparative Evaluation of Quality, Transparency, and Readability.Journal of pain research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to systematically evaluate five leading LLMs-ChatGPT, DeepSeek, Copilot, Gemini, and Perplexity-in providing MHD-related health information. The primary objectives were to determine (1) the reliability of MHD-related information generated by LLMs and (2) whether its readability meets the recommended standards for patient educational materials. Methods: A cross-sectional comparative design was adopted. The approximate timeframe during which the responses were generated was October 2025. Seventeen frequently asked MHD-related questions were identified using Google Trends and two online patient-caregiver forums. Each query was input into the five LLMs (ChatGPT-4o, Copilot, Gemini 2.5 Pro, Perplexity Pro, and DeepSeek-V3.2-Exp), and their responses were assessed using DISCERN, EQIP, JAMA, and GQS criteria for reliability, alongside FKGL, FRES, SMOG, CLI, ARI, and LWF readability indices. A heatmap analysis was also conducted to evaluate intra-model response variability. Results: High inter-rater reliability was confirmed between the two experts (ICC for average measures ranged from 0.851 to 0.879, all Conclusions: Current large language models (LLMs) exhibit significant variability in delivering maintenance hemodialysis information. While all five evaluated models demonstrated limitations in information quality, transparency, and readability, Perplexity performed relatively better overall. However, persistent deficiencies in source attribution, language accessibility, and response consistency limit their immediate clinical and educational utility. Future LLM development should prioritize readability optimization and context-aware customization to better support patient education.
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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.