With the growing prevalence of machine learning and artificial intelligence-based medical decision support systems, it is equally important to ensure that these systems provide patient outcomes in a fair and equitable fashion. This paper presents an innovative framework for detecting areas of algorithmic bias in medical-AI decision support systems. Our approach efficiently identifies potential biases in medical-AI models, specifically in the context of sepsis prediction, by employing the Classification and Regression Trees (CART) algorithm. We verify our methodology by conducting a series of synthetic data experiments, showcasing its ability to estimate areas of bias in controlled settings precisely. The effectiveness of the concept is further validated by experiments using electronic medical records from Grady Memorial Hospital in Atlanta, Georgia. These tests demonstrate the practical implementation of our strategy in a clinical environment, where it can function as a vital instrument for guaranteeing fairness and equity in AI-based medical decisions.

翻译：随着基于机器学习和人工智能的医疗决策支持系统的日益普及，确保这些系统以公平公正的方式提供患者预后结果同样重要。本文提出了一种新颖的框架，用于检测医学AI决策支持系统中的算法偏差区域。我们的方法通过采用分类与回归树（CART）算法，高效地识别医学AI模型中的潜在偏差，特别是在脓毒症预测场景中。我们通过一系列合成数据实验验证了该方法，展示了其在受控环境中精确估计偏差区域的能力。该概念的有效性进一步通过使用佐治亚州亚特兰大市格雷迪纪念医院的电子病历数据进行的实验得到验证。这些测试证明了我们的策略在临床环境中的实际应用性，可作为一种重要工具来确保基于AI的医疗决策的公平性与公正性。