ArticleJournal of medical Internet research2024
Evaluation of Prompts to Simplify Cardiovascular Disease Information Generated Using a Large Language Model: Cross-Sectional Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Evaluation of Large Language Models in Generating Physical Exercise Rehabilitation Programs for Musculoskeletal Disorders Across Multiple Clinical Scenarios.Healthcare (Basel, Switzerland) · 2026Article
- Evaluating large language models for simplifying non-English medical consent with clinician involvement.NPJ digital medicine · 2026Article
- Comparison of online radiologists and large language model chatbots in responding to common radiology-related questions in Chinese: a cross-sectional comparative analysis.Quantitative imaging in medicine and surgery · 2026Article
- Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.Diagnostics (Basel, Switzerland) · 2026Review
- Evaluation of large language models in generating and optimizing educational materials for neonatal home oxygen therapy.Frontiers in artificial intelligence · 2026Article
- Improving large language models accuracy for aortic stenosis treatment via Heart Team simulation: a prompt design analysis.European heart journal. Digital health · 2025Article
- Evaluating the Quality of Cardiovascular Disease Information From AI Chatbots: A Comparative Study.Cureus · 2025Article
- Quality and reliability of pediatric pneumonia related short videos on mainstream platforms: cross-sectional study.BMC public health · 2025Article
- Personalized insights into liver disease management: a text mining analysis of online consultation data.Frontiers in public health · 2025Article
- Augmentation or substitution: defining role of large language model in physical education.Frontiers in sports and active living · 2025Article
- Enhancing Patient Comprehension of Glomerular Disease Treatments Using ChatGPT.Healthcare (Basel, Switzerland) · 2024Article
- Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions.Journal of clinical medicine · 2024Review
- Prompt Engineering Paradigms for Medical Applications: Scoping Review.Journal of medical Internet research · 2024Article
- Large language models in patient education: a scoping review of applications in medicine.Frontiers in medicine · 2024Article
- How to design digital health interventions with artificial intelligence: A scoping review.Digital healthReview
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
In this cross-sectional study, we evaluated the completeness, readability, and syntactic complexity of cardiovascular disease prevention information produced by GPT-4 in response to 4 kinds of prompts.
Indexed as
Identifiers
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.