Accurate and interpretable diagnostic models are crucial in the safety-critical field of medicine. We investigate the interpretability of our proposed biomarker-based lung ultrasound diagnostic pipeline to enhance clinicians' diagnostic capabilities. The objective of this study is to assess whether explanations from a decision tree classifier, utilizing biomarkers, can improve users' ability to identify inaccurate model predictions compared to conventional saliency maps. Our findings demonstrate that decision tree explanations, based on clinically established biomarkers, can assist clinicians in detecting false positives, thus improving the reliability of diagnostic models in medicine.

翻译：在医学这一安全关键领域，准确且可解释的诊断模型至关重要。我们探究了基于生物标志物的肺超声诊断流程的可解释性，以增强临床医生的诊断能力。本研究旨在评估：相较于传统显著性图，利用生物标志物的决策树分类器所提供的解释，能否提升用户识别模型不准确预测的能力。研究结果表明，基于临床公认生物标志物的决策树解释，能够辅助临床医生检测假阳性结果，从而提升医学诊断模型的可靠性。