在基于证据的医学中合理使用AI决策支持:有效性,可解释性和责任性
1Department of Food and Resource Economics, University of Copenhagen, 1958Frederiksberg C, Denmark.
概括
临床医生需要解释不透明的人工智能 (AI) 在医疗决策中的输出,而不仅仅是验证. 了解AI推理对于负责任的医疗AI使用和基于证据的实践至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 不透明的机器学习 (ML) 模型提供准确的医学诊断,预后和治疗建议.
- 将这些人工智能工具负责任地整合到临床实践中,需要对它们的使用做出明确的理由.
研究的目的:
- 探索使用不透明AI输出在医疗决策中的理由.
- 为了比较AI在医学中的使用"解释视图"与"验证视图".
主要方法:
- 在医学中对人工智能辩护的哲学分析.
- 对解释视图而不是验证视图的论证.
- 临床认识论责任的检查.
主要成果:
- "解释性观点"认为临床医生需要理解AI输出推理.
- "验证观点"认为已建立的安全和可靠性标准足够.
- 纯粹的验证在基于证据的医学中被认为是不够的.
结论:
- 临床医生需要对不透明的AI输出进行解释,以确保负责任的医学决策.
- 临床医生的认识系统责任延伸到理解人工智能生成的建议.
- 单靠人工智能产出的结果无法在医疗保健中建立一个实际的行动过程.
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