Comparative Evaluation and Performance of Large Language Models in Clinical Infection Control Scenarios: A Benchmark Study.

ABSTRACT: Background: Infection prevention and control (IPC) in hospitals relies heavily on infection control nurses (ICNs) who manage complex consultations to prevent and control infections. This study evaluated large language models (LLMs) as artificial intelligence (AI) tools to support ICNs in IPC decision-making processes. Our goal is to enhance the efficiency of IPC practices while maintaining the highest standards of safety and accuracy. Methods: A cross-sectional benchmarking study at Queen Mary Hospital, Hong Kong assessed three LLMs-GPT-4.1, DeepSeek V3, and Gemini 2.5 Pro Exp-using 30 clinical infection control scenarios. Each model generated clarifying questions to understand the scenarios before providing IPC recommendations through two prompting methods: an open-ended inq

SUBMITTER: Wong SC 

PROVIDER: S-EPMC12563182 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature