评估翻译AI:一个双向移动目标问题
Richard K Leuchter1,2, William B Turner1, David Ouyang3
1Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
NEJM AI
|January 30, 2026
概括
卫生系统需要对预测性AI工具进行更好的监督. 与对照组实施随机部署可确保AI在患者护理中的安全性,有效性和公平性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 监管科学 监管科学
背景情况:
- 预测性人工智能 (AI) 模型在医疗系统中越来越多地使用.
- 不一致的监督会导致人工智能工具的合规性和监管差距,包括软件作为医疗设备.
- 现有的监督机制对于具有潜在护理影响的行政和运营人工智能模型是不够的.
研究的目的:
- 解决医疗保健中预测性AI模型监督的关键缺口.
- 突出"双向移动目标问题",破坏人工智能模型的自治.
- 为实施预测患者结果或利用的AI模型提出一个新的标准.
主要方法:
- 在AI实施中确定了合规性和监管缺口.
- 描述了并发干预混和行动诱导的结果偏见.
- 建议短期随机部署,使用对照组作为新标准.
主要成果:
- 由于"双向移动目标问题",传统的评估方法是不够的.
- 执行机构负有确保人工智能安全和有效性的责任.
- 随机部署为严格的AI评估提供了一个关键的反事实.
结论:
- 卫生系统必须采用人工智能实施的新标准.
- 短期随机部署对于评估AI性能和干预有效性至关重要.
- 这种方法确保人工智能工具是安全的,有效的,公平的,建立患者的信任.
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