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

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Summary
This summary is machine-generated.

Large language models (LLMs) show promise in assisting infection control nurses (ICNs) with decision-making, but current AI tools are not reliable for autonomous use. Critical errors highlight the need for LLMs to support, not replace, ICN expertise in infection prevention and control.

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evaluationinfection controllarge language models

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Infection Prevention and Control

Background:

  • Infection prevention and control (IPC) is crucial in hospitals, relying on infection control nurses (ICNs).
  • Evaluating large language models (LLMs) as artificial intelligence (AI) tools to support ICNs in IPC decision-making.
  • The goal is to enhance IPC efficiency, safety, and accuracy.

Purpose of the Study:

  • To benchmark the performance of three LLMs (GPT-4.1, DeepSeek V3, Gemini 2.5 Pro Exp) in clinical infection control scenarios.
  • To assess AI-generated recommendations' coherence, conciseness, usefulness, relevance, evidence quality, and actionability.
  • To evaluate the impact of different prompting methods and expert backgrounds on AI performance.

Main Methods:

  • A cross-sectional study involving 30 clinical infection control scenarios at Queen Mary Hospital, Hong Kong.
  • Three LLMs were tested using open-ended and structured prompting methods.
  • Sixteen experts (ICNs and physicians) rated the AI-generated recommendations on a 1-10 scale.

Main Results:

  • GPT-4.1 and DeepSeek V3 significantly outperformed Gemini 2.5 Pro Exp (p < 0.001).
  • Structured prompting improved evidence quality (p < 0.001); doctors rated outputs higher than nurses (38.83 vs. 32.06, p < 0.001).
  • Qualitative review revealed critical errors in clinical judgment and practical applicability across all models, posing safety risks.

Conclusions:

  • GPT-4.1 and DeepSeek V3 provide useful IPC advice but are not yet reliable for autonomous use.
  • LLMs cannot replace the expertise of ICNs due to critical deficiencies in clinical judgment.
  • LLMs should function as adjunct tools to support, not automate, clinical decision-making in IPC.