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在医疗保健机构的大型语言模型中对偏见评估的框架

Tara Templin^1,2,3, Sophia Fort⁴, Prasanna Padmanabham⁵

¹Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ttemplin@unc.edu.

NPJ digital medicine

|July 7, 2025

概括

一个新的审计框架解决了AI临床决策模型缺乏标准化评估的问题,确保准确性和公平性. 这种方法侧重于在医疗保健中负责任地采用人工智能的模型输出.

科学领域:

人工智能在医学中的应用
临床决策支持系统临床决策支持系统
医疗保健技术评估医疗保健技术评估

背景情况:

大型语言模型 (LLM) 提供了人工智能辅助临床决策的潜力,但缺乏标准化的评估方法.
目前的采用受到模型准确性,偏见和缺乏透明审计流程的担忧所阻碍.
确保医疗保健中人工智能工具的可靠性和公平性对于患者安全和有效的临床实践至关重要.

研究的目的:

引入一种新的,标准化的五步审计框架,用于评估临床决策支持中使用的LLM.
引导医疗保健从业人员评估AI模型的准确性和识别潜在的偏见.
促进AI在临床环境中的负责任开发和部署.

主要方法:

制定一个五步框架,包括利益相关者参与,对特定人群的模型校准和基于场景的测试.
创建开放式访问工具,以促进利益相关者参与审计过程.
通过实践审计示例来展示框架的应用.

主要成果:

拟议的框架为审计AI临床决策模型提供了一个结构化的方法.
它强调测试模型输出与临床相关的场景,以确保性能和识别偏差.
提供了开放式访问工具和一个示例审计,以帮助从业人员.

结论:

标准化的审计框架对于在临床决策中安全有效地采用LLM至关重要.
将审计重点放在模型输出上,而不是内部参数,可以促进负责任的人工智能使用.
该框架支持监管工作,并建立对人工智能医疗应用的信任.

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