Improving Model's Interpretability and Reliability using Biomarkers
This addresses the need for more interpretable and reliable diagnostic tools in medicine, though it appears incremental as it builds on existing biomarker and explanation methods.
The study tackled the problem of improving diagnostic model reliability in medicine by comparing decision tree explanations based on biomarkers to conventional saliency maps, finding that these explanations help clinicians detect false positives.
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.