了解和解决人工智能系统中的偏见:紧急医疗医生的一本手册
Ethan E Abbott1,2,3,4,5,6, Tehreem Rehman1, Anthony Rosania7
1Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Journal of the American College of Emergency Physicians open
|January 15, 2026
概括
在紧急医疗 (EM) 中的人工智能 (AI) 提供了好处,但存在风险偏见. 这一框架有助于紧急医生 (EP) 识别和减轻急诊部 (ED) 的AI偏见,以获得公平的患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健偏见 医疗保健偏见
- 人工智能在医学中的应用
背景情况:
- 人工智能 (AI) 工具在紧急医疗 (EM) 中越来越多地被采用,为提高效率,患者体验和安全提供了机会.
- 然而,人工智能集成带有风险,特别是嵌入算法或训练数据中的偏见的加剧,可能会影响不同患者群体的临床决策.
研究的目的:
- 为急诊医生 (EP) 提供一个实际的框架,以了解,识别和解决急诊部 (ED) 中使用的临床和操作AI工具中的偏差.
- 将人工智能偏见定义为导致不公平结果的系统缺陷,可能会扩大现有的医疗保健差距.
主要方法:
- 审查EMAI偏见的常见来源,包括数据,算法,测量和人与人互动因素.
- 人工智能偏见在EM实践中表现的说明性例子,例如分类工具,风险分层和医疗设备.
- 讨论监管环境,结构化评估框架 (部署前,监测,部署后),以及社会技术视角和利益相关者参与等关键原则.
主要成果:
- 人工智能偏见可能会对临床决策产生不利影响,特别是对脆弱患者群体.
- 了解数据和算法缺陷等来源对于识别潜在的陷至关重要.
- 结构化的评估框架和利益相关者的参与对于管理AI偏见至关重要.
结论:
- 紧急医生在通过倡导,验证,反和维持临床判断来缓解与人工智能相关的偏见方面发挥着关键作用.
- 有关人工智能工具的透明度和批判性评估的积极方法对于公平的紧急护理至关重要.
- 解决人工智能偏见对于实现人工智能在医疗保健中的全部潜力至关重要,而不会延续或放大健康不平等.
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