## 4 Results
### Post Hoc Explanation Methods
This suggests that the effect of each explanation method on diagnostic performance is method-dependent. One potential explanation could be that certain techniques introduce non-meaningful noise into the diagnostic process, which might distract the radiologist and reduce performance. On the other hand, methods that boost diagnostic outcomes may offer valuable insights that either confirm or encourage further investigation by the radiologist. We also requested that radiologists evaluate the utility of each method using a Likert scale ranging from one to five, where one signifies 'not useful' and five denotes 'extremely useful'. These data were employed to investigate any correlation between radiologists' perceived usefulness and the actual effectiveness of these methods. Furthermore, we sought to identify the preferred method among radiologists and the reasons behind their preference. The questionnaire asked radiologists to assess the usefulness of each method in terms of its ability to demonstrate the rationale behind the model's decision making. Table 4 showcases the outcomes of this evaluation. **What traits enhance explainability for radiologists? **  
This study incorporated a qualitative analysis of radiologists' feedback to better comprehend the factors that enhance the model's explainability. The goal is to offer insights into the characteristics of effective explanation methods, which can inform future development efforts. To analyze the textual responses, we employed thematic analysis and identified three recurring themes: Insight, Visualization Style, and Accuracy [38]. These themes were refined through discussion and chosen as the primary evaluation factors. The first theme, Insight, emerged from radiologists' comments on the value of explanation methods providing sufficient insights. Radiologists shared instances where, after reviewing the explanation method, they detected a lesion that they initially missed in the MRI image. This was especially helpful for small or challenging-to-detect lesions. Additionally, radiologists found it beneficial if the explanation method highlighted specific image areas for further analysis, and they gained confidence in their diagnosis when the method identified the same lesion they detected. Visualization Style was the second theme identified, where radiologists found noisy methods distracting despite potentially offering useful insights. They relied on color information to determine the significance of various image areas, aiding their reasoning process. Moreover, radiologists favored overlay visualizations over non-overlay ones, a preference consistent across the group. The third and final theme, Accuracy, emphasized the importance of explanation methods accurately localizing lesions. Radiologists expressed that accurate localization provided the most confidence in classifications. Highlighting the zone where a lesion was situated proved almost as useful as pinpointing the actual lesion. Accuracy was deemed \begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **Perceived Usefulness [95\% CI]** \\ \hline Integrated Gradients & 3.28 [2.21, 4.35] \\ Grad-CAM & 3.14 [2.10, 4.18] \\ DeepLIFT & 3.42 [2.97, 3.88] \\ SHAP & 2.0 [1.01, 2.98] \\ SmoothGrad & 2.85 [1.81, 3.89] \\ CXPlain & 3.28 [2.63, 3.93] \\ BayLIME & 3.8 [3.5, 4.49] \\ BLRP & 3.66 [3.08, 4.26] \\ DistDeepSHAP & 3.33 [2.89, 4.24] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Perceived usefulness reported by the cohort of radiologists. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **F1 Score** & **Sensitivity** & **Accuracy** \\ \hline Integrated Gradients & 0.58 & 0.59 & 0.58 \\ Grad-CAM & 0.57 & 0.71 & 0.42 \\ DeepLIFT & 0.75 & 0.83 & 0.75 \\ SHAP & 0.68 & 0.71 & 0.63 \\ SmoothGrad & 0.45 & 0.40 & 0.55 \\ CXPlain & 0.78 & 0.86 & 0.61 \\ BayLIME & 0.84 & 0.78 & 0.79 \\ BLRP & 0.49 & 0.55 & 0.43 \\ DistDeepSHAP & 0.79 & 0.69 & 0.79 \\ Radiologist & 0.59 & 0.72 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F1 score, sensitivity, and accuracy of each method.