人工智能和解释:如何,为什么,以及何时解释黑子.
1AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands.
European journal of radiology
|February 28, 2024
概括
即使是复杂的人工智能 (AI) 模型也可以被理解,类似于理解物理定律. 这种方法对于推进AI在医学,特别是放射学方面至关重要,确保信任和持续改进.
科学领域:
- 医疗人工智能 医疗人工智能
- 放射学人工智能的人工智能
- 可解释的人工智能 (XAI)
背景情况:
- 人工智能 (AI) 在科学,特别是医学和放射学中构成了"黑子"的挑战,阻碍了信任和知识创造.
- 人工智能算法的不可解释性对患者护理,错误识别和科学进步构成风险.
研究的目的:
- 论证理解"黑子"人工智能模型是可能的,与理解物理学中的自然规律进行并行.
- 为解释人工智能模型提供一个框架,强调人工智能开发人员和放射学家的合作作用.
- 探索可解释AI (XAI) 研究计划的贡献.
主要方法:
- 在理解"黑子"人工智能模型和解读物理中自然规律之间的类比.
- 讨论开发AI模型解释的过程.
- 专注于放射性AI场景中的实际应用.
主要成果:
- 了解复杂的AI,甚至是"黑子"模型,可以通过系统的调查来实现.
- 放射科医生在AI模型的持续改进和验证中发挥着关键作用.
- 人工智能开发人员和从业人员必须合作,以确保人工智能的安全和有效的整合.
结论:
- 对于理解不透明的AI算法存在希望,这对医疗应用至关重要.
- 涉及人工智能开发人员和医疗从业人员的协作方法对于可信的人工智能至关重要.
- 可解释性AI (XAI) 研究对于在放射学等敏感领域揭开AI的神秘面至关重要.
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