The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-making process, which is usually considered non-transparent and challenging to comprehend. In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods to explain the prediction of the black-box AI models in the thyroid nodule diagnosis application. We propose new statistic-based XAI methods, namely Kernel Density Estimation and Density map, to explain the case of no nodule detected. XAI methods' performances are considered under a qualitative and quantitative comparison as feedback to improve the data quality and the model performance. Finally, we survey to assess doctors' and patients' trust in XAI explanations of the model's decisions on thyroid nodule images.

翻译：将深度学习模型的预测结果向最终用户解释的能力，是利用人工智能（AI）赋能医疗决策过程的重要特性，而这一过程通常被认为缺乏透明度且难以理解。本文采用最先进的可解释人工智能（XAI）方法，在甲状腺结节诊断应用中解释黑箱AI模型的预测结果。我们提出了两种基于统计的新型可解释性方法——核密度估计（Kernel Density Estimation）与密度图（Density map），用于解释未检测到结节的情况。通过定性与定量比较，评估可解释性方法的性能，并以此作为提升数据质量与模型性能的反馈。最后，我们开展问卷调查，评估医生与患者对甲状腺结节图像模型决策的可解释性方法的信任程度。