需要平衡"黑子"系统和可解释的人工智能:在放射学中必须实施
Fabio De-Giorgio1, Beatrice Benedetti1, Matteo Mancino2
1Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Department of Healthcare Surveillance and Bioethics, Section of Legal Medicine, Università Cattolica del Sacro Cuore, Rome, Italy.
European journal of radiology
|March 3, 2025
概括
人工智能 (AI) 在放射学中提供了显著的好处,但面临着数据隐私和偏见等挑战. 可解释性AI (XAI) 对医疗成像中的透明度和伦理患者护理至关重要.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 人工智能 (AI),特别是机器和深度学习,通过处理大数据集和增强图像分析,正在改变放射学.
- 尽管AI具有潜力,但其与放射学集成面临重大障碍,包括数据隐私,知情同意和潜在的外部干扰.
- 人工智能算法的偏差,通常是由于不代表性数据或培训不足,可能导致结果偏差并加剧健康差异.
研究的目的:
- 检查人工智能对放射学的影响,突出其能力和挑战.
- 解决围绕医疗诊断中人工智能实施的伦理和法律考虑.
- 倡导采用可解释的人工智能 (XAI) 以提高透明度和患者理解.
主要方法:
- 审查目前在放射学中的AI应用,专注于机器学习和深度学习模型.
- 对道德和法律影响的分析,包括数据隐私,偏见和对人工智能引起的错误的问责制.
- 讨论"黑子"AI模型的局限性和可解释AI (XAI) 的好处.
主要成果:
- 人工智能在放射学中的图像分析方面表现出色,但存在诸如数据偏差,生成模型的"幻觉"和不透明的决策过程等风险.
- 医疗保健专业人员仍然对涉及人工智能的医疗错误负责,因为人工智能系统是支持工具,而不是取代人类判断.
- 许多人工智能模型的"黑盒子"性质阻碍了患者的知情同意,因为他们的推理缺乏透明度.
结论:
- 可解释的人工智能 (XAI) 对于提高AI驱动放射学的透明度至关重要.
- 优先考虑XAI允许患者了解AI在他们的护理中的作用,培养信任和道德实践.
- 通过确保人工智能对临床决策的影响是可以理解和合理的,即便性能与黑子模型相比略有降低,XAI也促进了伦理标准.
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