## 4 Results
### Uncertainty-Based Explanation Methods
#### 4.2.2 What Traits Enhance Explainability for Radiologists? We delve into the radiologists' qualitative feedback to gain insights into which attributes enhance explainability. This understanding enriches our comprehension of explanation methods and could inform future development in this area. The textual feedback from the questionnaire was subjected to thematic analysis, as outlined in [38]. The research team categorized the radiologists' responses into codified items, which were subsequently organized under shared themes. These themes laid the foundation for three key dimensions. Ultimately, the team identified four pivotal characteristics: (1) precision, (2) clarity, (3) presentation, and (4) consistency as illustrated in Figure 4.  
Figure 4: Characteristics that improve explainability for radiologists. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Understanding [95\% CI]** & **Confidence [95\% CI]** & **Justification [95\% CI]** \\ \hline CXPlain & 3.28 [2.63, 3.93] & 3.42 [2.59, 4.26] & 3.14 [2.10, 4.18] \\ BayLIME & 4.0 [3.50, 4.49] & 3.71 [2.89, 4.52] & 3.71 [3.06, 4.36] \\ BLRP & 3.85 [3.08, 4.26] & 3.71 [2.89, 4.52] & 3.42 [2.33, 4.51] \\ DistDeepSHAP & 3.57 [2.89, 4.24] & 3.28 [2.63, 3.93] & 3.14 [2.10, 4.18] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for perceived understanding, confidence, and justification. Understanding  
Understanding aimed to evaluate the usefulness of explanation methods in helping radiologists comprehend why a specific classification was made. Clarity of the explanation was crucial, and it was observed that a noisy heatmap could impede understanding. However, some noise was acceptable if it carried meaning, as it could assist the radiologist in identifying lesions that they might have initially missed. The presentation of the heatmap also played a significant role, with a gradient presentation like that of CXPlain preferred over a non-gradient presentation such as that of BayLIME. Radiologists used the colors of the gradient to determine which areas were more important. Regarding clarity, radiologists mentioned the importance of clear methods; otherwise, they could be distracting. For presentation, it is essential to display either a heatmap or binary map. Precision refers to how accurately the heatmap localizes the lesion. Finally, consistency relates to the radiologists' preference for a method that explains in a manner consistent with their own approach. This analysis provides valuable insights into the key factors that contribute to the effectiveness of explanation methods for medical imaging, which can inform the design of future techniques. Precision was important, with radiologists stating that if a lesion was highlighted in the explanation, they could immediately understand why the classification was made. Consistency between the radiologist's own explanations and those generated by the explanation method was also essential for understanding. This highlights the need for explanation methods to be clear, meaningful, precise, and consistent with radiologists' own thought processes to effectively aid their understanding of classification decisions. Confidence  
We also aimed to assess the degree of confidence that the explanation methods instilled in the radiologists concerning the classification. Precision was crucial in this regard, with radiologists noting that observing the deep learning model classify the MRI as containing a lesion and having the explanation method highlight the same lesion gave them the most confidence. They found it reassuring to see the explanation method emphasize the same lesion they had identified themselves. Consistency was also important; if the explanation method highlighted the zone where the lesion was located, it was almost as beneficial as highlighting the lesion itself. The radiologists desired to use the explanation method to verify the classification with confidence.