ArticleFrontiers in public health2026
Decoupled quality and readability in skin cancer education from large language models.
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. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Performance comparison of large language models for medication counseling in people living with HIV.Frontiers in public health · 2026Article
- Assessment of vaccine information accuracy across large language models.Frontiers in public health · 2026Article
- Patient education for neuromyelitis optica spectrum disorder using large language models: combining expert assessment and real-world patient interaction.Frontiers in physiology · 2026Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Large language models (LLMs) are increasingly used by the public to obtain health information, yet the relationship between content quality and readability in LLM-generated patient education remains unclear. Methods: We benchmarked five LLMs (Doubao, DeepSeek, Wenxin Yiyan, Tongyi Qianwen, and GPT-5) using an identical set of 20 Mandarin Chinese skin-cancer FAQs (100 total outputs). Quality was assessed using c-PEMAT-P and the Global Quality Scale (GQS), and readability was assessed using seven indices (ARI, FRES, GFOG, FKGL, CL, SMOG, and LW). Group differences and correlations were evaluated with appropriate statistical tests. Results: Models showed comparable understandability/actionability (c-PEMAT-P), while overall quality (GQS) differed, with GPT-5 scoring highest. Readability varied substantially by both model and content category, and no single model performed best across all readability metrics. Correlation analyses indicated that quality and readability were largely decoupled. Discussion: High-quality outputs do not necessarily have high readability. Optimizing AI-generated skin-cancer education requires multi-faceted strategies that jointly consider model choice and content topic.
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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.