ArticlePediatric investigation2026
Large-language-models for pediatric diagnosis: Performance evaluation using real-world clinical notes from common and rare cases.
Article in Pediatric investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Beyond benchmark accuracy: Toward trustworthy, open, and agentic clinical artificial intelligence.Pediatric investigation · 2026Article
- Beyond accuracy metrics: Toward responsible integration of large language models in pediatric diagnostic reasoning.Pediatric investigation · 2026Article
- Research on intellectual property rights and legal liability definition of artificial intelligence-assisted pediatric disease diagnosis system.Frontiers in public health · 2026Article
- Clinical research on secure multi-party computation technology for auxiliary respiratory pathogen diagnosis and treatment design in preschool-aged children.Frontiers in pediatrics · 2026Article
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13 authors.
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Abstract
Importance: Rigorous evaluation of large language models (LLMs) in pediatric diagnosis using authentic clinical presentations remains limited, particularly regarding response consistency and rare disease recognition. Objective: To evaluate the diagnostic accuracy, consistency, and clinical usability of LLMs as diagnostic support tools in pediatric medicine compared with human clinicians using real-world cases. Methods: This cross-sectional study at Sant Joan de Déu Barcelona Children's Hospital evaluated four LLMs [DxGPT/GPT-4 (0613), Claude-3.5 Sonnet, GPT-4o (0513), and o1-preview] against 78 pediatric clinicians using 50 real clinical cases (25 rare diseases, 25 common conditions) from a single tertiary pediatric center. All cases were presented using Spanish intake-style clinical summaries. Each case was queried three times per LLM and evaluated by clinicians with different experience levels. Performance was assessed using the Top-1 and Top-5 diagnostic accuracy, response consistency (intraclass correlation coefficient), and qualitative evaluation. Extended clinical information was provided for 20 cases to assess the diagnostic efficiency. Results: Advanced LLMs significantly outperformed the clinicians in terms of diagnostic accuracy. o1-preview and Claude-3.5 Sonnet achieved mean Top-1 accuracies of 60.0% and 59.0%, respectively, compared to clinicians' 48.2% (odds ratios [ORs]: 2.99 and 2.75, both Interpretation: In this proof-of-concept study of a reference care center, newer LLMs outperformed previous models and human clinicians in complex pediatric diagnostics, particularly for rare diseases. These findings support further evaluation as augmentative diagnostic tools in similar settings, with appropriate legal, ethical, and clinical oversight frameworks.
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