Evaluating the Performance of AI Large Language Models in Detecting Pediatric Medication Errors Across Languages: A Comparative Study.

ABSTRACT: Objectives: This study aimed to evaluate the performance of four AI models, (GPT-5, GPT-4, Microsoft Copilot, and Google Gemini), in detecting medication errors through pediatric case scenarios. Methods: A total of 60 pediatric cases were analyzed for the presence of medication errors, of which only half contained errors. The cases covered four therapeutic systems (respiratory, endocrine, neurology, and infectious). The four models were exposed to the cases in both English and Arabic using a unified prompt. The responses for each model were used to calculate various performance metric cover accuracy, sensitivity, specificity and reproducibility. Analysis was carried out using SPSS version 22. Results: Microsoft Copilot demonstrated relatively higher accuracy (86.7% in

SUBMITTER: Abu-Farha RK 

PROVIDER: S-EPMC12786879 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature