ArticleCureus2024
Assessing the Quality of Patient Education Materials on Cardiac Catheterization From Artificial Intelligence Chatbots: An Observational Cross-Sectional Study.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Customized Education Sheets Generated by ChatGPT Improve Parental Visit Satisfaction and Procedural Knowledge Prior to Pediatric Cardiac Catheterization.Pediatric cardiology · 2026Article
- A comparative evaluation of the quality of responses provided by different large language model chatbots to frequently asked questions regarding nerve blocks.BMC anesthesiology · 2026Article
- A cross-sectional evaluation of large language model chatbot interfaces for patient-facing herpes zoster information: safety, information quality, and readability.Frontiers in public health · 2026Article
- Accuracy and Reliability of AI Models in Emergency Myocardial Infarction Education.Emergency medicine international · 2026Article
- Benchmarking the readability, quality, and educational suitability of large language models in communicating pertussis.Frontiers in public health · 2026Article
- Evaluating large language models using the Type 2 Diabetes Health Education guideline: a comparative analysis of ChatGPT-4.1, Claude-4.0, DeepSeek-V3, and ERNIE Bot 4.5 Turbo.Frontiers in public health · 2026Article
- Evaluation of the readability, quality, and accuracy of AI chatbot responses to questions about deleterious oral habits.BMC oral health · 2025Article
- Quality assessment of patient-facing urologic telesurgery content using validated tools.Journal of robotic surgery · 2025Article
- Artificial intelligence in pediatric dental trauma: do artificial intelligence chatbots address parental concerns effectively?BMC oral health · 2025Article
- Observational
- Quality evaluation of AI-generated diabetes-related health education texts from different generative models.Digital healthArticle
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Health literacy empowers patients to participate in their own healthcare. Personal health literacy is one's ability to find, understand, and use information/resources to make well-informed health decisions. Artificial intelligence (AI) has become a source for the acquisition of health-related information through large language model (LLM)-driven chatbots. Assessment of the readability and quality of health information produced by these chatbots has been the subject of numerous studies to date. This study seeks to assess the quality of patient education materials on cardiac catheterization produced by AI chatbots. Methodology We asked a set of 10 questions about cardiac catheterization to four chatbots: ChatGPT (OpenAI, San Francisco, CA), Microsoft Copilot (Microsoft Corporation, Redmond, WA), Google Gemini (Google DeepMind, London, UK), and Meta AI (Meta, New York, NY). The questions and subsequent answers were utilized to make patient education materials on cardiac catheterization. The quality of these materials was assessed using two validated instruments for patient education materials: DISCERN and the Patient Education Materials Assessment Tool (PEMAT). Results The overall DISCERN scores were 4.5 for ChatGPT, 4.4 for Microsoft Copilot and Google Gemini, and 3.8 for Meta AI. ChatGPT, Microsoft Copilot, and Google Gemini tied for the highest reliability score at 4.6, while Meta AI had the lowest with 4.2. ChatGPT had the highest quality score at 4.4, while Meta AI had the lowest with 3.4. ChatGPT and Google Gemini had Understandability scores of 100%, while Meta AI had the lowest with 82%. ChatGPT, Microsoft Copilot, and Google Gemini all had Actionability scores of 75%, while Meta AI had one of 50%. Conclusions ChatGPT produced the most reliable and highest quality materials, followed closely by Google Gemini. Meta AI produced the lowest quality materials. Given the easy accessibility that chatbots provide patients and the high-quality responses that we obtained, they could be a reliable source for patients to obtain information about cardiac catheterization.
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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.