ArticleCureus2025
Evaluating the Quality of Cardiovascular Disease Information From AI Chatbots: A Comparative Study.
Article in Cureus, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review.European heart journal. Digital health · 2026Review
- Development of the perceived AI-physician consistency and trust scale for cardiology: a Delphi-based study.Frontiers in digital health · 2026Article
- Article
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
Artificial intelligence (AI) is increasingly being utilized as an informational resource, with chatbots attracting users for their ability to generate instantaneous responses. This study evaluates the understandability, actionability, readability, quality, and misinformation in medical information provided by four prominent chatbots - Bard, ChatGPT 3.5, Claude 2.0, and Perplexity - on three prevalent cardiovascular diseases (CVDs): myocardial infarctions, heart failure, and arrhythmias. These chatbots were used because of their popularity and high usage rates among chatbots. Using Google Trends, the top five U.S. search queries related to heart attack, arrhythmia, and heart failure from September 29, 2018, to September 29, 2023, were identified. The top five queries were chosen in relation to these topics because they accounted for over 80% of the public's searches related to these topics. The chatbot responses were blinded and analyzed by two evaluators using DISCERN for quality, Patient Education Materials Assessment Tool (PEMAT) for understandability and actionability, and Flesch-Kincaid scores for readability. Statistical tests included the Kruskal-Wallis test for DISCERN, the chi-square test for PEMAT, and one-way ANOVA for Flesch-Kincaid scores. Bard generated responses with a statistically lower Flesch-Kincaid reading score than the other chatbots. Bard and ChatGPT 3.5 provided more actionable responses. Among the CVD topics, "heart attack" yielded lower-grade-level responses and more actionable information compared to "arrhythmia" and "heart failure." This study is among the first to assess AI credibility in disseminating cardiovascular information. It highlights how acute pathologic events may prompt more actionable and accessible chatbot responses. As AI continues to evolve, collaboration among healthcare professionals, researchers, and developers is crucial to ensuring the safe and effective use of AI in patient education and public health.
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What OpenQuestion holds
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.