ArticleFrontiers in public health2026
Evaluating large language models for myocardial infarction public health education: a comparative study on information quality, transparency and readability.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Objective: Myocardial infarction (MI) is an acute, life-threatening cardiovascular disease, and high-quality, accessible public health education is vital for emergency management. This study systematically evaluates the quality, transparency, clinical accuracy, patient safety, and readability of information generated by different large language models (LLMs) in responding to MI-related public inquiries. Methods: Twenty-five representative MI patient education questions were submitted to Gemini 3.5 Flash, Claude Opus 4.8, and ChatGPT 5.5. The generated information was independently evaluated by two cardiologists using four validated tools (DISCERN, EQIP, GQS, and JAMA) alongside a strict clinical safety assessment. Text readability was concurrently assessed utilizing six established metrics (FRES, ARI, GFI, CLI, FKGL, and SMOG). Results: Significant variations were observed in the quality, transparency and readability of information generated by the evaluated LLMs. Regarding quality and transparency, significant overall differences were noted among models in DISCERN ( Conclusion: While LLMs can generate structurally clear and logically coherent foundational content for MI-related queries, they occasionally produce clinically inappropriate directives. Furthermore, the texts generated by these models are overly complex, creating substantial reading barriers for the general public. Consequently, under zero-shot and English-language testing conditions, the current LLMs are not yet capable as standalone health education tools for MI.
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