基于模拟的ChatGPT对医疗保健相关感染监测的评估,使用经过验证的病例情景
Kelly Holmes1, Xiaoyu Liu2, Mishga Moinuddin1
1Infection Prevention & Management Associates, Inc. 5773 Woodway Drive, PMB 215; Houston, Texas 77057.
American journal of infection control
|February 2, 2026
概括
像ChatGPT这样的大型语言模型 (LLM) 在使用国家医疗保健安全网络 (NHSN) 定义的复杂医疗保健相关感染 (HAI) 监测中显示出有限的可靠性. 虽然显示出基于实验室的事件的潜力,但当前的人工智能工具在预防感染方面面临着多步逻辑的挑战.
科学领域:
- 预防和控制感染的预防和控制.
- 医疗保健中的人工智能
- 公共卫生监督 公共卫生监督
背景情况:
- 医疗相关感染 (HAI) 的监测至关重要,但复杂且资源密集.
- 国家医疗保健安全网络 (NHSN) 的定义是HAI监控的标准.
- 大型语言模型 (LLM) 为人工智能辅助监控提供了潜在的途径,但需要验证.
研究的目的:
- 评估ChatGPT (GPT-4) 在应用NHSN定义中的准确性和一致性.
- 与感染预防专家 (IPs) 相比,对AI的性能进行基准测试.
- 探索LLM在HAI监控中的潜力和局限性.
主要方法:
- 使用20项测试银行进行横截面模拟研究.
- 三个独立运行的ChatGPT (GPT-4) 使用2024年NHSN手册.
- 将ChatGPT响应与验证的答案密钥和22个IP进行比较.
主要成果:
- 在三次运行中,ChatGPT实现了45%的平均准确性.
- 在所有尝试中,只有20%的项目得到正确答案.
- 在实验室识别的事件中,ChatGPT的表现更好,但在时间或多步逻辑方面遇到了困难.
结论:
- 在此模拟中,ChatGPT-4在复杂的NHSN监控任务中表现出有限的可靠性.
- 人工智能工具显示了结构化,基于实验室的HAI场景的潜力.
- 研究结果表明,人工智能在预防感染方面存在当前的限制和未来的机遇.
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