医疗AI推理过程的剖析通过医生和生成AI合作
Alex J DeGrave1,2, Zhuo Ran Cai3, Joseph D Janizek1,2
1Paul G. Allen School of Computer Science and Engineering, University of Washington.
medRxiv : the preprint server for health sciences
|June 9, 2023
概括
这项研究引入了一个用于审计人工智能 (AI) 医疗设备的新框架,使其决策过程可解释. 这项研究揭示了皮肤病学中的AI如何使用预期和意想不到的视觉特征,增强对AI医疗保健的信任和安全.
科学领域:
- 医学的人工智能 (AI)
- 可解释的人工智能 (XAI)
- 医学成像分析 医学成像分析
背景情况:
- 大多数人工智能医疗设备的功能是"黑子",对用户和开发人员缺乏透明度.
- 对于可解释的人工智能来说,对于确保患者安全和临床信任来说,有着至关重要的需求.
- 皮肤病学是人工智能部署的一个关键领域,需要清楚地了解其诊断过程.
结论:
- 该框架提供了基于机器学习的医学图像AI推理的第一个医学可解释的观点.
- 这种方法为专业领域的严格可解释的人工智能应用创造了先例.
- 允许从业者,临床医生和监管机构以医学上可访问的方式理解复杂的AI推理.
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