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.

本研究使用最先进的可解释人工智能方法，如核密度估计和密度图来解释甲状腺结节诊断应用程序中黑匣子的人工智能模型的预测，以提高医学决策过程的透明度和可理解性，并评估医生和患者对于该模型决策的 XAI 解释的信任度。