ArticleFrontiers in public health2025
Effects of education level on natural language processing in cardiovascular health communication.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
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Authors and funding
6 authors.
Funding
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Abstract
Introduction: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, underscoring the importance of accessible health communication. Artificial intelligence (AI) tools such as ChatGPT and MediSearch have potential to bridge knowledge gaps, but their effectiveness depends on both accuracy and readability. This study evaluated how natural language processing (NLP) models respond to CVD-related questions across different education levels. Methods: Thirty-five frequently asked questions from reputable sources were reformatted into prompts representing lower secondary, higher secondary, and college graduate levels, and entered into ChatGPT Free (GPT-4o mini), ChatGPT Premium (GPT-4o), and MediSearch (v1.1.4). Readability was assessed using Flesch-Kincaid Ease and Grade Level scores, and response similarity was evaluated with BERT-based cosine similarity. Statistical analyses included ANOVA, Kruskal-Wallis, and Pearson correlation. Results: Readability decreased significantly with increasing education level across all models ( Discussion: These findings indicate that while NLP models adjust readability by education level, output complexity often exceeds average literacy, highlighting the need for refinement to optimize AI-driven patient education.
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