人工智能作为一种干预措施:改善临床结果依赖于人工智能开发和验证的因果关系方法
Shalmali Joshi1, Iñigo Urteaga2,3, Wouter A C van Amsterdam4
1Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
Journal of the American Medical Informatics Association : JAMIA
|January 8, 2025
概括
医疗保健人工智能 (AI) 开发应该优先考虑临床结果而不是标准指标. 专注于因果关系可以确保人工智能干预带来可行的改进和更好的患者结果.
科学领域:
- 医疗保健 人工智能 医疗保健 人工智能
- 临床信息学 临床信息学
- 因果推理因果推理
背景情况:
- 目前的医疗人工智能 (AI) 开发通常依赖于使用AUROC和DICE等指标的回顾性评估.
- 这些传统指标的高性能并不能保证在现实世界医疗保健环境中改善临床结果.
研究的目的:
- 倡导修订人工智能开发管道,优先考虑临床相关结果.
- 引入因果关系的概念在AI模型开发和验证医疗保健.
- 确保医疗保健人工智能是可操作的,并且可以明显改善患者的治疗结果.
主要方法:
- 提出一个逆向设计方法,从所需的临床结果开始.
- 将因果推理原则集成到人工智能开发,评估和验证过程中.
- 分析人工智能诱导的行为对临床相关结果的因果关系.
主要成果:
- 标准AI指标可能不反映真正的临床实用性.
- 一个因果框架对于开发有效和可操作的医疗保健AI至关重要.
- 为利益相关者提供建议,以加强人工智能对临床结果的影响.
结论:
- 医疗保健人工智能开发必须将重点从代用指标转移到临床结果的可证明改进.
- 采用因果推理方法对于创建人工智能至关重要,这将导致患者护理的积极和可衡量的变化.
- 在人工智能管道中的利益相关者参与,以因果关系原则为指导,将最大限度地提高人工智能在医疗保健中的有益影响.
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