ArticleCureus2024
Unlocking Health Literacy: The Ultimate Guide to Hypertension Education From ChatGPT Versus Google Gemini.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Application of AI in Hypertension Health Education: Scoping Review.Journal of medical Internet research · 2026Article
- Article
- A multi-layer retrieval-augmented large language model framework for enhancing hypertension education.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Comparison of different artificial intelligence tools' answers to questions related to early intervention: ChatGPT versus Gemini.Revista da Associacao Medica Brasileira (1992) · 2026Article
- Large Language model (LLM) in temporomandibular disorder education: a comparative study.BMC oral health · 2025Article
- Evaluating the performance of five large language models in answering Delphi consensus questions relating to patellar instability and medial patellofemoral ligament reconstruction.BMC musculoskeletal disorders · 2025Article
- A Comparative Analysis of GPT-3.5, GPT-4, GPT-4 Omni, Gemini Advanced, and Gemini 1.5 in Answering Frequently Asked Questions Regarding High Tibial Osteotomy.Orthopaedic journal of sports medicine · 2025Article
- Evaluating the performance of AI chatbots in responding to dental implant FAQs: A comparative study.BMC oral health · 2025Article
- Applications of large language models in cardiovascular disease: a systematic review.European heart journal. Digital health · 2025Review
- Artificial Intelligence in Peripheral Artery Disease Education: A Battle Between ChatGPT and Google Gemini.Cureus · 2025Article
- Assessment of patient information guides generated by LLMs for common cardiological procedures.Global cardiology science & practice · 2025Article
- Performance of artificial intelligence chatbots in responding to the frequently asked questions of patients regarding dental prostheses.BMC oral health · 2025Article
- Accuracy of LLMs in medical education: evidence from a concordance test with medical teacher.BMC medical education · 2025Article
- Quality of Chatbot Responses to the Most Popular Questions Regarding Erectile Dysfunction.Urology research & practice · 2025Article
- Effects of education level on natural language processing in cardiovascular health communication.Frontiers in public health · 2025Article
- Application of Generative Artificial Intelligence in Dyslipidemia Care.Journal of lipid and atherosclerosis · 2025Review
- Article
- Readability and Appropriateness of Responses Generated by ChatGPT 3.5, ChatGPT 4.0, Gemini, and Microsoft Copilot for FAQs in Refractive Surgery.Turkish journal of ophthalmology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Google Gemini (Google, Mountain View, CA) represents the latest advances in the realm of artificial intelligence (AI) and has garnered attention due to its capabilities similar to the increasingly popular ChatGPT (OpenAI, San Francisco, CA). Accurate dissemination of information on common conditions such as hypertension is critical for patient comprehension and management. Despite the ubiquity of AI, comparisons between ChatGPT and Gemini remain unexplored. Methods ChatGPT and Gemini were asked 52 questions derived from the American College of Cardiology's (ACC) frequently asked questions on hypertension, following a specified prompt. Prompts included: no prompting (Form 1), patient-friendly prompting (Form 2), physician-level prompting (Form 3), and prompting for statistics/references (Form 4). Responses were scored as incorrect, partially correct, or correct. Flesch-Kincaid (FK) grade level and word count were recorded. Results Across all forms, scoring frequencies were as follows: 23 (5.5%) incorrect, 162 (38.9%) partially correct, and 231 (55.5%) correct. ChatGPT showed higher rates of partially correct answers than Gemini (p = 0.0346). Physician-level prompts resulted in a higher word count across both platforms (p < 0.001). ChatGPT showed a higher FK grade level (p = 0.033) in physician-friendly prompting. Gemini exhibited a significantly higher mean word count (p < 0.001); however, ChatGPT had a higher FK grade level across all forms (p > 0.001). Conclusion To our knowledge, this study is the first to compare cardiology-related responses from ChatGPT and Gemini, two of the most popular AI chatbots. The grade level for most responses was collegiate level, which was above average for the National Institutes of Health (NIH) recommendations, but on par with most online medical information. Both chatbots responded with a high degree of accuracy, with inaccuracies being rare. Therefore, it is reasonable that cardiologists suggest either chatbot as a source of supplementary 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.