通过因果图利用生成性AI进行可解释的临床决策
Mehmet Eren Ahsen1, Rand Kittani2, Travis Gerke3
1Gies College of Business.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
生成性AI为临床AI创建可解释的结构因果模型 (SCM),改进因果推理. 这些人工智能驱动的SCM在估计COVID-19治疗效果方面,表现与人类专家相提并论.
科学领域:
- 人工智能在医学中的应用
- 因果推理因果推理
- 医疗信息学 医疗信息学
背景情况:
- 临床AI采用受到缺乏可解释性的阻碍.
- 生成型人工智能为医疗知识整合提供了潜力.
- 结构因果模型 (SCM) 对于可靠的因果推断至关重要.
研究的目的:
- 开发一个使用生成AI的计算框架,以创建可解释的SCM用于临床应用.
- 加强临床决策支持,质量改善和人口健康管理.
- 为了弥合基于证据的医学临床AI的解释性差距.
主要方法:
- 一个使用中西部医疗保健会议因果图挑战数据集的案例研究.
- 基于变压器的大型语言模型 (LLM) 与人类性能的比较.
- 目标试验模拟以使用SCMs估计COVID-19治疗对死亡率的影响.
- 与已发表的随机对照试验结果 (RECOVERY试验) 进行基准测试.
主要成果:
- 人工智能设计的SCM在大多数COVID-19患者的严重程度层实现了>90%的启动覆盖率.
- 人工智能和人类模型都显示出相当的临床可信性和类似的统计性能.
- 基于SCM的方法显示覆盖率明显高 (76-98%) 比传统方法 (1-37%).
结论:
- 由AI生成的可解释性SCM可以在临床环境中促进可靠的因果推断.
- 该框架允许有意义的人类-人工智能协作,同时保持方法严格.
- SCM是提高临床AI采用率和可信度的有希望的解决方案.
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