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：人类完全可理解模型的需求正日益被视为人工智能研究的核心主题。当AI模型具备可解释性时，其在敏感领域辅助决策的接受度将逐步提升，而即将出台的法规将进一步强化这一可解释模型的发展趋势。可解释AI的关键应用场景之一是医疗实践——通过本质上建立信任的精准决策支持方法，医疗领域可从中获益。本研究提出一种融合概率树与模糊逻辑的新方法FPT（MedFP），用于辅助临床实践。该方法完全可解释，使临床医生能够生成、控制和验证整个诊断流程；其核心优势在于通过提供不确定性估计与反事实分析来降低误诊频率。我们以概念验证形式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疾病进展风险预测。结果表明，概率模糊决策树可为临床医生提供可解释的决策支持；此外，在概率模型中引入模糊变量能显著呈现传统概率决策树中因刚性阈值设定而丢失的细微差异。研究表明，FPT及其预测结果可通过专为此设计的用户友好界面，以直观方式辅助临床实践。同时，本文对FPT模型的可解释性进行了深入探讨。