不受监管的大型语言模型产生类似医疗器械的输出
Gary E Weissman1,2,3,4, Toni Mankowitz5, Genevieve P Kanter5,6
1Palliative and Advanced Illness Research (PAIR) Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. gary.weissman@pennmedicine.upenn.edu.
NPJ digital medicine
|March 7, 2025
概括
大型语言模型对临床决策支持有希望,但需要监管. 测试显示,这些人工智能模型可以生成类似于设备的临床决策支持输出.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 医疗器械规则 医疗器械规则
背景情况:
- 大型语言模型 (LLM) 显示了加强临床决策支持 (CDS) 系统的巨大潜力.
- 目前,没有基于LLM的工具获得了食品和药物管理局 (FDA) 的授权,用于作为CDS设备.
- 将先进的人工智能集成到医疗保健中需要对其能力和监管影响进行彻底评估.
研究的目的:
- 评估诱导大型语言模型 (LLM) 产生适用于临床决策支持 (CDS) 的输出的可行性.
- 为了确定当前的LLM是否可以在各种临床场景中产生类似于设备的CDS输出.
- 在临床实践中对LLM的潜在部署进行监管考虑.
主要方法:
- 评估两个突出的大型语言模型 (LLM).
- 测试LLM在产生临床决策支持 (CDS) 输出方面的表现.
- 在各种模拟的临床场景中分析LLM反应.
主要成果:
- 大型语言模型 (LLM) 证明了产生类似于设备类临床决策支持 (CDS) 的输出的能力.
- 在多个经过测试的临床场景中观察到一致的决策支持生成.
- 该研究表明,LLM可以很容易地模仿已批准的CDS设备的功能.
结论:
- 这些发现表明,大型语言模型 (LLM) 能够提供类似于设备的临床决策支持 (CDS).
- 随着LLM轻松产生CDS类输出,这突显了当前监管框架中的潜在缺口.
- 临床使用LLM的正式部署需要FDA等机构制定适当的监管监督和授权流程.
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