为什么我们确实需要医疗保健的可解释AI
Giovanni Cinà1,2, Tabea E Röber3, Rob Goedhart3
1Department of Medical Informatics, Amsterdam University Medical Center, Amsterdam, The Netherlands. g.cina@amsterdamumc.nl.
Diagnostic and prognostic research
|December 2, 2025
概括
可解释的人工智能 (AI) 对于值得信赖的医疗保健应用至关重要,尽管人们对其可靠性存在担忧. 这项研究认为,可解释的人工智能对于人机交互和预防临床环境中失去控制至关重要.
科学领域:
- 医学的人工智能 (AI)
- 临床信息学 临床信息学
- 人与计算机的交互
背景情况:
- 医疗保健中认证的人工智能工具的采用正在增加,引发了争论.
- 旨在提高人工智能的透明度和信任的可解释AI (XAI) 面临着对其在医疗应用中的可靠性的审查.
- 医疗人工智能专家们对XAI技术的使用和标准化存在担忧.
研究的目的:
- 提供一个平衡的观点,解释AI在医疗保健的实用性.
- 为了解决有关XAI技术可靠性的批评.
- 在临床应用和医疗保健干预中将XAI置于背景中.
主要方法:
- 对现有文献和专家意见的批判性审查关于可解释的人工智能在医疗保健.
- 分析AI在临床环境中的特定要求和挑战.
- 医学AI中人机交互的哲学和实践检查.
主要成果:
- 尽管存在合理的担忧,但这篇文章认为可解释人工智能的持续重要性.
- 可解释的人工智能被介绍为有效的人机交互的关键组成部分.
- 严格的临床验证本身不足以防止AI系统的潜在控制损失.
结论:
- 可解释的人工智能研究计划仍然是人工智能在医疗保健中安全有效整合的核心.
- 可解释的人工智能作为一个有价值的工具,以减轻与人工智能采用相关的风险,特别是关于用户控制.
- 需要一个平衡的方法来利用XAI的好处,同时承认其在医疗领域的局限性.
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