在医疗保健中,可解释与可解释的人工智能:如何实现理解?
1Bern University of Applied Sciences, Switzerland.
Studies in health technology and informatics
|May 17, 2025
概括
本研究探讨了医疗聊天机器人中人工智能可解释性的需求,提出了一个框架,使用丹尼尔·丹内特的故意立场来提高用户的理解,并指导人工智能开发和监管.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 医疗信息学 医疗信息学
背景情况:
- 深度学习的采用需要人工智能可解释性,特别是医学的大型语言模型 (LLM).
- 目前的人工智能解释缺乏明确性,特别是聊天机器人的医疗建议.
- 区分解释,解释和理解对于人工智能决策支持至关重要.
研究的目的:
- 探索AI决策支持中的解释,解释和理解的细微差别.
- 提出一种分析人工智能解释的方法,使用故意立场.
- 检查人工智能解释方法对医疗聊天机器人开发和监管的影响.
主要方法:
- 对AI解释,解释和理解的概念分析.
- 应用丹尼尔·丹内特的故意立场作为一个分析框架.
- 检查对医疗人工智能系统的影响.
主要成果:
- 在人工智能系统中,在解释,解释和理解之间建立了一个明确的区别.
- 意图立场为分析AI解释提供了一种新的方法.
- 拟议的方法可以提高用户参与度和对人工智能驱动的医疗建议的理解.
结论:
- 人工智能可解释性对于值得信赖的医疗人工智能至关重要,特别是基于LLM的聊天机器人.
- 刻意的立场为开发和规范医疗AI提供了一个有价值的镜头.
- 需要进一步的研究来完善AI在临床实践中的解释方法.
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