ArticleFrontiers in public health2026
Comparative evaluation of the quality, reliability, and readability of five large language model responses to frequently asked questions on gestational hypertension.
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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Abstract
Objective: This study aims to clarify the impact of Large Language Models (LLMs) and health education content categories on generated text quality (patient education appropriateness and overall quality) and readability, providing empirical evidence for the standardized application of LLMs-assisted health communication. Methods: Five mainstream models (Doubao, Deep Seek, Wenxin Yiyan, Gemini and GPT-5) were selected to generate 100 texts (20 per model, 20 per theme) across five health education categories: disease cognition dimension, etiology and risk factors dimension, diagnosis and examination dimension, treatment and management dimension, and prevention and prognosis dimension. Test quality was assessed using the Chinese version of the Patient Education Material Readability Assessment Scale (C-PEMAT) and the Global Quality Scale (GQS), while readability was measured via seven metrics including the Automated Readability Index (ARI) and the Flesch Reading Ease Score (FRES). Correlation analyses were used to explore relationships among indicators. Results: Our analysis revealed clear hierarchical performance across five large language models: GPT-5 achieved the highest scores in both patient education appropriateness (C-PEMAT: 11.10 ± 2.40) and overall text quality (GQS: 5.00 [4.00, 5.00]). GPT-5 exhibited significantly higher GQS scores than all other models ( Conclusion: This study demonstrates significant hierarchical performance among LLMs in health science text creation. Different health education themes show partial indicator variation but stable overall quality. Notably, quality and readability are relatively independent (with weak correlations), providing empirical evidence for understanding LLMs in health popularization.
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