ArticleCureus2025
Artificial Intelligence in Peripheral Artery Disease Education: A Battle Between ChatGPT and Google Gemini.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- A Comparison of a Customized Peripheral Artery Disease (PAD)-Specific Generative AI Chatbot and General-Purpose AI Chatbots for PAD Patient Education.Journal of clinical medicine · 2026Article
- Expert evaluation of GPT-4o and Gemini responses to patient questions on carotid endarterectomy.Revista da Associacao Medica Brasileira (1992) · 2026Article
- Tests of large language models' medical competence and application for clinical decision support of musculoskeletal rehabilitation.Frontiers in digital health · 2025Article
- Evaluating Artificial Intelligence Conversational Platforms for Parental Queries on Antenatal Hydronephrosis: A Comparative Blinded Assessment.Journal of Indian Association of Pediatric SurgeonsArticle
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
Background Peripheral artery disease (PAD) is a prevalent yet often overlooked manifestation of atherosclerosis that significantly contributes to cardiovascular morbidity and mortality. With the increasing reliance on artificial intelligence (AI) for medical information, it is essential to assess the accuracy and readability of AI-generated health content, especially with regard to common cardiovascular diseases. Objective This study evaluates the accuracy, completeness, and readability of responses generated by OpenAI's ChatGPT (San Francisco, CA) and Google's Gemini (Mountain View, CA) when answering common questions about PAD. AI responses were compared to Cleveland Clinic's frequently asked questions (FAQs) on PAD to assess the reliability of AI-generated responses as a patient education tool. Methods ChatGPT 4.0 and Gemini 1.0 were prompted in three formats (no prompt (Form 1), patient-level prompt (Form 2), and physician-level prompt (Form 3)) before answering 19 questions from Cleveland Clinic's FAQs on PAD. Responses were categorized as correct, partially correct, or incorrect based on percent content alignment. Readability was assessed using the Flesch-Kincaid (FK) grade level, and word count differences were analyzed. Chi-square tests and one-way analysis of variance (ANOVA) were used for statistical analysis, with a significance threshold of p < 0.05. Results ChatGPT provided 70% correct and 30% partially correct responses, with no incorrect answers. Gemini provided 52% correct, 45% partially correct, and 3% incorrect responses. ChatGPT performed significantly better in accuracy, with a p-value < 0.05. FK analysis showed no significant readability differences between the two chatbots (mean FK grade: ChatGPT, 10.81; Gemini, 10.73), although both were higher than the recommended reading level for patient education. ChatGPT's responses were significantly longer than Gemini's, with a p-value < 0.0001. Conclusion Both ChatGPT and Gemini provided mostly accurate and comprehensive responses to commonly asked questions about PAD, demonstrating their potential use as supplementary education tools for patients with appropriate provider oversight. However, the grade reading level of these materials exceeded the recommended reading levels set forth by national guidelines, which warrants improvement in AI-driven health communication. Given the growing reliance on AI in healthcare, further research should explore ways to enhance AI-generated medical content for broader patient accessibility and evaluate its impact on patient outcomes.
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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.