ArticleCureus2024
Evaluating ChatGPT Responses on Atrial Fibrillation for Patient Education.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Performance of large language models in mitral valve surgery patient education: a comparative analysis.BMC medical informatics and decision making · 2026Article
- Assessing AI-generated smoking cessation advice for patient education in primary care.BMC primary care · 2026Article
- A systematic review of the limitations of large language models in generating healthcare content.PLOS digital health · 2026Article
- Improving the Readability of Institutional Heart Failure-Related Patient Education Materials Using GPT-4: Observational Study.JMIR cardio · 2025Observational
- Applications of large language models in cardiovascular disease: a systematic review.European heart journal. Digital health · 2025Review
- Evaluating performance of large language models for atrial fibrillation management using different prompting strategies and languages.Scientific reports · 2025Article
- A Primer on Large Language Models (LLMs) and ChatGPT for Cardiovascular Healthcare Professionals.CJC open · 2025Review
- Effects of education level on natural language processing in cardiovascular health communication.Frontiers in public health · 2025Article
- Article
- Enhancing AI Chatbot Responses in Health Care: The SMART Prompt Structure in Head and Neck Surgery.OTO openArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background ChatGPT is a language model that has gained widespread popularity for its fine-tuned conversational abilities. However, a known drawback to the artificial intelligence (AI) chatbot is its tendency to confidently present users with inaccurate information. We evaluated the quality of ChatGPT responses to questions pertaining to atrial fibrillation for patient education. Our analysis included the accuracy and estimated grade level of answers and whether references were provided for the answers. Methodology ChatGPT was prompted four times and 16 frequently asked questions on atrial fibrillation from the American Heart Association were asked. Prompts included Form 1 (no prompt), Form 2 (patient-friendly prompt), Form 3 (physician-level prompt), and Form 4 (prompting for statistics/references). Responses were scored as incorrect, partially correct, or correct with references (perfect). Flesch-Kincaid grade-level unique words and response lengths were recorded for answers. Proportions of the responses at differing scores were compared using the chi-square analysis. The relationship between form and grade level was assessed using the analysis of variance. Results Across all forms, scoring frequencies were one (1.6%) incorrect, five (7.8%) partially correct, 55 (85.9%) correct, and three (4.7%) perfect. Proportions of responses that were at least correct did not differ by form (p = 0.350), but perfect responses did (p = 0.001). Form 2 answers had a lower mean grade level (12.80 ± 3.38) than Forms 1 (14.23 ± 2.34), 3 (16.73 ± 2.65), and 4 (14.85 ± 2.76) (p < 0.05). Across all forms, references were provided in only three (4.7%) answers. Notably, when additionally prompted for sources or references, ChatGPT still only provided sources on three responses out of 16 (18.8%). Conclusions ChatGPT holds significant potential for enhancing patient education through accurate, adaptive responses. Its ability to alter response complexity based on user input, combined with high accuracy rates, supports its use as an informational resource in healthcare settings. Future advancements and continuous monitoring of AI capabilities will be crucial in maximizing the benefits while mitigating the risks associated with AI-driven 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.