合成数据,合成信任:在数字革命中应对数据挑战
Arman Koul1, Deborah Duran2, Tina Hernandez-Boussard3
1School of Medicine, Stanford University, Stanford, CA, USA.
The Lancet. Digital health
|December 2, 2025
概括
在医疗AI中过度依赖合成数据可能会导致偏见和性能差. 建议对医疗保健中人工数据的负责任使用采取新的保障措施,以确保公平性和临床有效性.
科学领域:
- 人工智能 (AI) 是一种人工智能.
- 医疗人工智能 医疗人工智能
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健中对人工智能 (AI) 的日益依赖往往是由假设更大的数据集可以提高模型性能所推动的.
- 合成数据生成被广泛用于增强现实世界的数据,解决短缺问题,但存在偏差和减少概括性的风险.
- 在医疗AI中快速采用合成数据导致了"合成信任",对可能缺乏临床有效性或人口代表性模型的毫无根据的信心.
研究的目的:
- 倡导谨慎地使用合成数据来训练临床AI算法.
- 为负责开发和部署合成医疗AI提出可行的保障措施.
- 确保医疗保健人工智能应用使用合成数据的端到端问责制,数据完整性和公平性.
主要方法:
- 对医疗人工智能合成数据增强当前实践的审查.
- 识别与过度使用合成数据相关的潜在风险,包括偏差传播和模型退化.
- 制定保障措施的框架,包括数据标准,测试协议和部署披露.
主要成果:
- 过度使用合成数据可能会导致传播偏见,加速模型退化和受损的概括性.
- "合成信任"的现象凸显了对在合成数据上训练的人工智能模型的不合理信任的危险.
- 拟议的保障措施包括为培训数据制定明确的标准,实施脆弱性测试,并强制披露合成数据来源.
结论:
- 在使用合成数据用于临床AI模型培训时,必须谨慎.
- 实施拟议的保障措施可以减轻与合成数据相关的风险,确保数据完整性和公平性.
- 这些措施为医疗保健应用中对合成数据的负责任和公平使用制定了新的标准.
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