在个性化癌症治疗中利用可解释AI的知识:挑战和未来方向
Emilia Daghir-Wojtkowiak1, Javier Alfaro1, Michele Mastromattei2
1International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland.
Frontiers in digital health
|October 15, 2025
概括
这项研究引入了知识图表,使AI驱动的医疗决策对临床医生来说可以理解. 这种方法通过提高复杂数据整合的透明度来增强个性化医疗,以便更好地选择治疗.
科学领域:
- 医疗保健中的人工智能
- 个性化医疗是个性化的医疗.
- 生物信息学是一种生物信息学.
背景情况:
- 整合多模式患者数据对于个性化医疗至关重要.
- 人工智能驱动的决策支持系统需要基于证据,并根据临床指南进行更新.
- 将复杂的AI输出翻译为临床理解是一个主要的采用挑战.
研究的目的:
- 介绍开发基于知识的可解释的人工智能系统的组件和挑战.
- 展示一个可以解释的人工智能系统的努力,支持癌治疗决策.
- 在临床实践中解决透明人工智能的需求.
主要方法:
- 使用知识图来整合多omics数据并将其表示为相互连接的网络.
- 采用人工智能模型,逐步完善知识图以提高透明度.
- 为可解释的人工智能系统开发基础设施.
主要成果:
- 知识图表有助于整合多样化的患者数据.
- 可解释的人工智能通过突出影响力特征和关系来提高透明度.
- 凯蒂联盟正在开发临床应用的基础设施.
结论:
- 知识图是创建可解释AI的关键,用于临床决策支持.
- 可解释的人工智能系统可以改善在医疗保健中采用人工智能的情况.
- 这项工作支持癌患者的个性化治疗决策.
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