ArticlePediatric research2025
Can large language models assist with pediatric dosing accuracy?
Article in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Assessing the capability of large language models in answering pediatric critical care board-style questions.Scientific reports · 2026Article
- Comparison of the performance of ChatGPT-5, Gemini 3, Copilot, Perplexity, and medical students in answering neurology questions: a cross-sectional study.Scientific reports · 2026Article
- An Evaluation Framework for Large Language Models in Clinical Nursing: A Scoping Review and Expert Consultation.Journal of nursing management · 2026Article
- Evaluating the Performance of AI Large Language Models in Detecting Pediatric Medication Errors Across Languages: A Comparative Study.Journal of clinical medicine · 2025Article
- Evaluation of ChatGPT-4o and DeepSeek as tools for orthodontic health literacy in public dental education.Scientific reports · 2025Article
- Assessing the Capability of Large Language Models in Answering Pediatric Critical Care Board-Style Questions.Research square · 2025Article
- Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.Clinical and experimental pediatrics · 2025Article
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4 authors.
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Abstract
BACKGROUND AND
objectiveMedication errors in pediatric care remain a significant healthcare challenge despite technological advancements, necessitating innovative approaches. This study aims to evaluate Large Language Models' (LLMs) potential in reducing pediatric medication dosage calculation errors compared to experienced nurses.
methodsThis cross-sectional study (June-August 2024) involved 101 nurses from pediatric and neonatal departments and three LLMs (ChatGPT-4o, Claude-3.0, Llama 3 8B). Participants completed a nine-question survey on pediatric medication calculations. Primary outcomes were accuracy and response time. Secondary measures included seniority and group membership on accuracy.
resultsSignificant differences (P < 0.001) were observed between nurses and LLMs. Nurses averaged 93.14 ± 9.39 accuracy. Claude-3.0 and ChatGPT-4o achieved 100 accuracy, while Llama 3 8B was 66 accurate. LLMs were faster (15.7-75.12 seconds) than nurses (1621.2 ± 8379.3 s). The Generalized Linear Model analysis revealed task performance was significantly influenced by duration (Wald χ² = 27,881.261, p < 0.001) and interaction between relative seniority and group membership (Wald χ² = 3,938.250, p < 0.001), with participants achieving a mean total grade of 91.03 (SD = 13.87).
conclusionsClaude-3.0 and ChatGPT-4o demonstrated perfect accuracy and rapid calculation capabilities, showing promise in reducing pediatric medication dosage errors. Further research is needed to explore their integration into practice. IMPACT: Key Message Large Language Models (LLMs) like ChatGPT-4o and Claude-3.0 demonstrate perfect accuracy and significantly faster response times in pediatric medication dosage calculations, showing potential to reduce errors and save time. Addition to Existing Literature This study provides novel insights by quantitatively comparing LLM performance with experienced nurses, contributing to the understanding of AI's role in improving medication safety. Impact The findings emphasize the value of LLMs as supplemental tools in healthcare, particularly in high-stakes pediatric care, where they can reduce calculation errors and improve clinical efficiency.
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