为医疗健康AI系统设计可解释性评估框架，提升医生信任度。
Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-being
- 构建面向健康AI的可解释性评估框架
- 通过案例研究验证框架实用性
- 适用于高影响个体决策的AI系统
人工智能在计算机系统开发中的融合带来新挑战：如何使智能系统对人类可解释。这一问题在健康与福祉领域尤为重要，透明的决策支持系统有助于医护人员理解并信任自动化判断与预测。为此，亟需工具指导可解释AI系统的开发。本文提出一个专为健康与福祉领域设计的可解释AI系统评估框架，并通过案例研究展示其实际应用。我们认为该框架不仅适用于医疗AI开发，也可用于任何对个人产生重大影响的AI系统。
原文摘要 · Abstract (English)
The integration of Artificial Intelligence in the development of computer systems presents a new challenge: make intelligent systems explainable to humans. This is especially vital in the field of health and well-being, where transparency in decision support systems enables healthcare professionals to understand and trust automated decisions and predictions. To address this need, tools are required to guide the development of explainable AI systems. In this paper, we introduce an evaluation framework designed to support the development of explainable AI systems for health and well-being. Additionally, we present a case study that illustrates the application of the framework in practice. We believe that our framework can serve as a valuable tool not only for developing explainable AI systems in healthcare but also for any AI system that has a significant impact on individuals.
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