人工智能陷和不该做的事情:减轻人工智能偏见
Judy Wawira Gichoya1, Kaesha Thomas1, Leo Anthony Celi2,3,4
1Department of Radiology, Emory University, Atlanta, United States.
The British journal of radiology
|September 12, 2023
概括
医疗保健中的人工智能 (AI) 可以延续偏见. 本研究回顾了人工智能偏见陷和放射学缓解策略,强调了人工智能的整个生命周期中的人类和机器因素.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 放射学人工智能的人工智能
背景情况:
- 人工智能 (AI) 应用越来越多地被整合到医疗保健系统中.
- 人工智能模型的失败揭示了它们的潜力,使偏见永久化.
- 企业级AI部署的变化可能会影响AI模型的性能.
研究的目的:
- 提供已知的陷导致人工智能偏见的最新审查.
- 讨论在放射学应用中减轻AI偏差的策略.
- 考虑企业AI部署对AI模型性能的影响.
主要方法:
- 对医疗保健中人工智能偏见的现有文献的审查.
- 分析AI生命周期阶段:问题定义,数据策划,模型培训和部署.
- 在更广泛的人工智能部署背景下,框架偏见陷.
主要成果:
- 人工智能偏见源于各种人类和机器因素.
- 导致偏见的陷在整个AI生命周期中被确定.
- 缓解策略对于在医疗保健中负责地部署人工智能至关重要.
结论:
- 优先考虑偏差评估和缓解对于放射学中的AI至关重要.
- 了解企业AI与个人模型性能之间的相互作用至关重要.
- 解决偏见需要采用整体方法,考虑整个AI生命周期.
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