ArticleCureus2025
Assessing Information Provided by ChatGPT: Heart Failure Versus Patent Ductus Arteriosus.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 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
- ChatGPT Applications in Heart Failure: Patient Education, Readability Enhancement, and Clinical Utility.Journal of cardiovascular development and disease · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction The study aims to provide insights regarding the effectiveness of ChatGPT in providing accurate, complete, and understandable information related to heart failure (HF) and patent ductus arteriosus (PDA). The study aimed to identify whether the abundance of online resources and patient inquiries leads to ChatGPT providing more accurate, complete, and understandable responses for HF compared to the less common PDA. Methodology Ten open-ended questions related to HF and 11 questions related to PDA were formed, and answers generated in ChatGPT 3.5 were assessed for accuracy via a 6-point Likert scale and for completeness via a 3-point Likert scale. Understandability was assessed using the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P). Data analysis and visualization was conducted using R Software version 4.3.1. Statistical significance between accuracy and completeness for both the conditions was determined using Spearman's R and Mann-Whitney U tests. Results Relevant answers were generated for all questions on the first attempt. For HF-related answers, the mean accuracy score was 5.4 (SD=0.7) and the mean completeness score was 2.3 (SD=0.7), with a median understandability of 83.75% (IQR=77.8%-94.5%). Spearman's correlation coefficient (rs) for HF was 0.44854 (p=0.19353). For PDA, the mean accuracy was 5.09 (SD=0.7), completeness was 2.27 (SD=0.5), and median understandability was 94.5% (IQR=88.9%-100%). Spearman's correlation coefficient for PDA was 0.21409 (p=0.52731). Furthermore, the Mann-Whitney U-test revealed no significant differences in accuracy (p=0.35758) or completeness (p=0.88866) between the two conditions. Conclusions While the accuracy and completeness of answers related to HF were slightly higher compared to those for PDA, the understandability of PDA responses was notably better. There is a need for refining artificial intelligence-driven language models for medical information dissemination.
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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.