ArticleFrontiers in artificial intelligence2025
Assessing large language models as assistive tools in medical consultations for Kawasaki disease.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evaluating ChatGPT-4o as an Educational Support Tool for the Emergency Management of Dental Trauma: Randomized Controlled Study Among Students.JMIR medical education · 2025Trial
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- LLMs in Medical Education for Autism Caregivers: A Comparative Evaluation of Accuracy, Readability, Actionability, and Neurodiversity-Affirming Language.Healthcare (Basel, Switzerland) · 2026Article
- Large-language-models for pediatric diagnosis: Performance evaluation using real-world clinical notes from common and rare cases.Pediatric investigation · 2026Article
- An integrated and interpretable machine learning framework for Kawasaki disease diagnosis and risk prediction.Translational pediatrics · 2025Article
- Reliability of ChatGPT answers to common questions on developmental dysplasia of the hip as an information source for parents.Frontiers in pediatrics · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Kawasaki disease (KD) presents complex clinical challenges in diagnosis, treatment, and long-term management, requiring a comprehensive understanding by both parents and healthcare providers. With advancements in artificial intelligence (AI), large language models (LLMs) have shown promise in supporting medical practice. This study aims to evaluate and compare the appropriateness and comprehensibility of different LLMs in answering clinically relevant questions about KD and assess the impact of different prompting strategies. Methods: Twenty-five questions were formulated, incorporating three prompting strategies: No prompting (NO), Parent-friendly (PF), and Doctor-level (DL). These questions were input into three LLMs: ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. Responses were evaluated based on appropriateness, educational quality, comprehensibility, cautionary statements, references, and potential misinformation, using Information Quality Grade, Global Quality Scale (GQS), Flesch Reading Ease (FRE) score, and word count. Results: Significant differences were found among the LLMs in terms of response educational quality, accuracy, and comprehensibility ( Conclusion: This study indicates that LLMs have great potential in providing information about KD, but their use requires caution due to quality inconsistencies and misinformation risks. Significant discrepancies existed across LLMs and prompting strategies. Claude 3.5 Sonnet offered the best response quality and accuracy, while Gemini 1.5 Pro excelled in comprehensibility. PF prompting with Claude 3.5 Sonnet is most recommended for parents seeking KD information. As AI evolves, expanding research and refining models is crucial to ensure reliable, high-quality information.
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