弥合差距:一个实际的逐步方法,以保证在医疗保健中安全实施大型语言模型
Jessica D Workum1,2,3, Davy van de Sande1,3, Diederik Gommers1,3
1Department of Adult Intensive Care, Erasmus MC University Medical Center, Rotterdam, Netherlands.
Frontiers in artificial intelligence
|February 11, 2025
概括
本文提供了在医疗保健中安全实施大型语言模型 (LLM) 的实用指南. 它提供了一步一步的方法和ACUTE记忆力,用于在临床环境中负责任地采用AI.
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
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 大型语言模型 (LLM) 对医疗保健应用有希望,包括管理任务和临床决策支持.
- 对于在医疗保健机构安全和负责任地实施LLM的可操作指南存在重大差距.
- 医疗机构和提供者需要明确的框架来有效地导航LLM集成.
研究的目的:
- 为医疗保健中负责任和安全地实施LLM提出一个实际的,逐步的方法.
- 为医疗保健组织和提供者提供可行的建议,以整合LLMs.
- 建立一个结构化的方法来评估临床实践中的LLM输出.
主要方法:
- 制定涵盖患者隐私,模型调整,超参数调整和快速工程的建议.
- 区分临床决策支持 (CDS) 和非CDS的LLM应用程序.
- 引入ACUTE记忆法 (准确性,一致性,语义不变输出,可追溯性,伦理考虑) 用于评估LLM响应.
- 为LLM实施提出一个模型治理结构.
主要成果:
- 一套全面的建议,安全的LLM整合在医疗保健.
- 一种结构化的方法,即ACUTE记忆法,用于评估LLM输出的可靠性和安全性.
- 关于将LLM适应医疗保健特定需求并确保伦理考虑的指导.
结论:
- 拟议的框架为医疗保健组织提供了一个明确的途径,以负责任地实施LLM.
- 遵守这些准则可以提高患者的安全性和对人工智能驱动的医疗保健解决方案的信任.
- 系统的评估和治理对于最大化临床实践中LLM的好处至关重要.
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