ArticleBMC medical informatics and decision making2026
Evaluation of large Language models on pediatric asthma: a comparative study of Claude3-Opus, Gemini 2.0, ChatGPT-4o, and DeepSeek-a cross-sectional questionnaire study.
Article in BMC medical informatics and decision making, 2026. 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.
- Assessment of vaccine information accuracy across large language models.Frontiers in public health · 2026Article
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7 authors.
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Abstract
backgroundArtificial intelligence (AI) has shown potential for enhancing medical practice and improving patient outcomes. However, the efficacy and linguistic accessibility of Large Language Models(LLMs) in pediatric asthma management remain underexplored. This study evaluated the performance of four LLMs in generating clinical information within this domains.
methodsWe administrated 15 guideline-based pediatric asthma inquiries to hatGPT-4o, Claude 3 Opus, Gemini 2.0, and DeepSeek. Anonymized responses were independently evaluated by three board-certified pediatric pulmonologists using DISCERN instrument (score range 16–80). Readability was assessed using six standard indices. Inter-rater reliability was measured with intraclass correlation coefficients (ICC). Statistical analysis included repeated measures and post-hoc comparisons with effect size reporting.
resultsNo significant difference was found in the overall quality of health information (DISCERN scores) among the four LLMs (F(3,56) = 0.144, p =.933, η² =0.008), with all mean scores clustered within a narrow “fair-to-good” range (50.3–51.9). However, significant differences were observed in readability: ChatGPT-4o generated significantly more comprehensible text than DeepSeek (FRE mean difference = 12.41, p =.005, Cohen’s d = 1.28), while DeepSeek performed significantly worse than all other models (all p <.05). Inter-rater reliability was high (ICC range: 0.849–0.901, all p <.001). Critically, the mean readability level of all outputs (FKGL: 13.2–14.9) far exceeded the recommended reading accessibility level for patient materials.
conclusionsWhile current LLMs can provide generally accurate information on pediatric asthma, their outputs exhibit significant limitations in readability for patient-facing use. ChatGPT‑4o shows relative advantages in comprehensibility, yet none meet recommended health-literacy standards. These findings underscore that AI should serve as a supplementary decision‑support tool under clinician supervision, not as a substitute for professional medical advice. Future work should prioritize the integration of adaptive text‑simplification features, validate AI‑generated content in real‑world clinical and caregiver settings, and expand evaluations to include emerging models and diverse chronic disease contexts.
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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.