Can large language models assist with pediatric dosing accuracy?

ABSTRACT:

Background and objective

Medication 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.

Methods

This 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.

Results

Significant differences (P < 0.001) were observed between nurs

SUBMITTER: Levin C 

PROVIDER: S-EPMC12602315 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature