## 3 Results
### XAI Evaluation
#### 3.8.2 Quantitative  
The QT metrics focus mainly on semantics and generally comprised standardized performance metrics in the field of AI-based image analysis. Classification performance is quantified by confusion matrix scores, e.g., accuracy, precision, sensitivity, specificity, F1-score, or area under the receiver operating characteristic curve (AUROC). Accuracy is the most common and provides a general indication of the proportion of correct predictions (true positives and true negatives) out of all class predictions made. Segmentation performance is quantified using DSC, determining the overlap between the predicted and ground truth mask segments. Scores range from 0 (no overlap) to 1 (perfect overlap), indicating the model's success in delineating the tumor boundaries within the images. Localization performance is quantified with the common intersection over union (IoU) metric. The metric evaluates how precisely the tumors are localized within the image, considering both the accuracy of the boundary box and the class prediction. These standard metrics provide quantitative insights by statistically evaluating the AI model's performance in intended tasks. They are essential for tuning the model, understanding its strengths and weaknesses, and comparing its performance with other AI models. However, these metrics primarily measure the performance of AI models itself and do not quantify the impact of XAI on improving model explainability in these evaluations. A few studies involved clinicians during the XAI evaluation phase [40; 44; 47; 49; 53]. [PERSON] et al. (2023) conducted reader studies with nine physicians who used heat maps generated by their XAI Explainer method to assist in diagnosing 200 test set US images (100 malignant) [49]. The physicians initially diagnosed without XAI assistance to establish a baseline; thereafter, they used the heat maps to aid their decisions. Performance improvements were measured using accuracy, precision, recall, F1-score, and AUROC curves, showing higher performance with the Explainer visualizations, especially among junior physicians. In [PERSON] et al. (2021), a two-part reader study with seven radiologists (14 years of experience on average) compared the performance of BI-RADS-Net with and without Grad-CAM heat maps. Reviewing 152 breast lesions (44 malignant) from 141 patients in the test set, the radiologists initially diagnosed without XAI assistance and then reassessed using the heat maps. Their study found that visualizations with heat maps improved sensitivity, specificity, and overall diagnostic accuracy, highlighting the technique's added value in clinical practice [47]. Uncommon QT evaluation metrics for XAI methods were found in several studies:  
* Shapley values are derived from game theory and provide a method to distribute the impact among contributors (features) in a cooperative game (prediction model). Each feature value's contribution is determined by assessing the change in prediction when a feature is added or removed for all possible combinations of features. The aim is to fairly attribute the model's output to its input features, providing insights into which features are most important for predictions. In three studies, SHAP values were used to determine which clinical features (e.g., tumor size, shape, or texture) most significantly impact the models' classification of a tumor as benign or malignant [43; 44; 48]. * The zero-mean normalized cross-correlation (ZNCC) score presents a statistical measure to assess the similarity between two images. It calculates the degree of similarity between two images through a normalized cross-correlation measurement formula, subtracting their mean and dividing by their standard deviation. The ZNCC score ranges from \(-1\) to \(1\), where \(1\) indicates perfect correlation, \(0\) indicates no correlation, and \(-1\) indicates perfect inverse correlation. In [PERSON] et al.