临床预测模型的市场后监测的挑战
Sardar Ansari1, Brittany Baur1, Karandeep Singh2
1Department of Emergency Medicine, University of Michigan, Ann Arbor, MI.
NEJM AI
|August 28, 2025
概括
人工智能 (AI) 模型的部署后性能由于干预的混而具有挑战性. 在动态的临床环境中,先进的因果模型可能为人工智能提供可靠的验证.
科学领域:
- 临床信息学
- 医学的人工智能
- 医疗服务研究
背景情况:
- 预测性人工智能 (AI) 模型对于临床工作流程至关重要,包括预测和决策支持.
- 由于数据集的变化,部署后的性能下降是人工智能模型面临的重大挑战.
- 目前的监管指导方针要求持续监测,但人工智能绩效评估的实际策略缺乏.
研究的目的:
- 解决预测人工智能模型在部署后的性能评估方面的挑战.
- 要突出混医疗干预影响人工智能绩效评估的问题.
- 探索潜在的解决方案以及对医疗保健中的人工智能可靠验证方法的需求.
主要方法:
- 讨论部署后的评估挑战,重点是医疗干预.
- 建议解决方案的审查,例如保留模型输出,结果监测,并包括模型中的干预.
- 探索高级因果建模作为潜在的验证方法.
主要成果:
- 混的医疗干预可以导致人工智能性能评估偏差,错误地表明模型衰变.
- 现有的解决方案存在道德或实际限制,阻碍有效的部署后评估.
- 缺乏有效的监控导致潜在的有害,未经评估的人工智能模型.
结论:
- 先进的因果建模为人工智能模型的可靠后部署验证提供了一个有希望的方法.
- 在动态的临床环境中,考虑因果关系的基于证据的决策对于人工智能模型更新至关重要.
- 开发和验证有效的部署后监测方法至关重要,以确保持续的AI实用性和预防患者的伤害.
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