The need for fully human-understandable models is increasingly being recognised as a central theme in AI research. The acceptance of AI models to assist in decision making in sensitive domains will grow when these models are interpretable, and this trend towards interpretable models will be amplified by upcoming regulations. One of the killer applications of interpretable AI is medical practice, which can benefit from accurate decision support methodologies that inherently generate trust. In this work, we propose FPT, (MedFP), a novel method that combines probabilistic trees and fuzzy logic to assist clinical practice. This approach is fully interpretable as it allows clinicians to generate, control and verify the entire diagnosis procedure; one of the methodology's strength is the capability to decrease the frequency of misdiagnoses by providing an estimate of uncertainties and counterfactuals. Our approach is applied as a proof-of-concept to two real medical scenarios: classifying malignant thyroid nodules and predicting the risk of progression in chronic kidney disease patients. Our results show that probabilistic fuzzy decision trees can provide interpretable support to clinicians, furthermore, introducing fuzzy variables into the probabilistic model brings significant nuances that are lost when using the crisp thresholds set by traditional probabilistic decision trees. We show that FPT and its predictions can assist clinical practice in an intuitive manner, with the use of a user-friendly interface specifically designed for this purpose. Moreover, we discuss the interpretability of the FPT model.

翻译：完全可理解的模型作为AI研究的核心主题正日益受到重视。当这些模型具有可解释性时，它们在敏感领域辅助决策的接受度将不断提高，而这一趋势也将因即将出台的法规而进一步强化。可解释AI的关键应用之一便是医疗实践，通过内在建立信任的精准决策支持方法，该领域能够从中获益。本文提出了一种名为FPT（MedFP）的新方法，该方法将概率树与模糊逻辑相结合以辅助临床实践。这一方法完全可解释，能让临床医生生成、控制并验证整个诊断流程；该方法的优势之一是能够通过提供不确定性估计和反事实分析来降低误诊频率。作为概念验证，我们将该方法应用于两个真实医疗场景：甲状腺恶性结节分类与慢性肾病患者进展风险预测。结果表明，概率模糊决策树可为临床医生提供可解释性的支持；此外，在概率模型中引入模糊变量能带来传统概率决策树因固定阈值设定而丢失的重要细微差异。我们展示了FPT及其预测如何通过专门设计的用户友好界面以直观方式辅助临床实践，并进一步讨论了FPT模型的可解释性。