## 5 Conclusions and Limitations  
This paper aimed to investigate the effectiveness of explanation methods in improving the lesion detection performance in radiology and gain insights into what radiologists deem important for effective explanation methods. A pilot cohort of ten radiologists was presented with MRI images and corresponding explanation techniques, as well as MRI images without explanations for baseline comparison. The results show that the effectiveness of explanation methods can vary depending on the specific technique used. The highest-rated method in terms of perceived usefulness was DeepLIFT, while the lowest-rated method was SHAP. Interestingly, DeepLIFT also resulted in the largest marginal increase in accuracy, while the lowest was SmoothGrad. The radiologists noted that insight, visualization style, and accuracy were important components of effective explanation methods. Furthermore, we found that uncertainty-based explanation methods can increase the lesion detection performance by up to a 20% increase in the F1 score. We found that BayLIME had the highest justification score among the explanation methods, with radiologists noting that it effectively highlighted the lesion's location. However, the BayLIME visualization style was the least preferred, with CXPlain being the preferred visualization style among the radiologists. CXPlain also had the highest confidence score, tied with BayLIME, but had the lowest understanding and justification scores. This was because CXPlain marked many areas of the prostate that did not include relevant information, resulting in many false positives. The radiologists identified several components that they considered crucial in effective explanation methods, such as precise lesion highlighting, clarity, accurate localization, presentation style, consistency, and noise reduction. They found gradient-based presentation styles such as CXPlain to be more useful compared to BayLIME's presentation style. The radiologists noted that noisy methods were difficult to use, but the uncertainty component helped them reason through the noise when present. They also stressed the importance of providing explanations that are consistent with their preferred method of explanation. These findings highlight the potential of uncertainty-based explanation methods in improving lesion detection performance in radiology. However, the effectiveness of these methods can vary depending on the specific technique used, and it is important to consider multiple factors such as precision, clarity, presentation, consistency, insight, visualization style, and accuracy in designing effective explanation methods. These insights can guide future research efforts in developing effective and explainable AI models in radiology. This study aimed to scrutinize the utility of various post hoc explanation methods in radiology, focusing on lesion detection in MRI images and understanding why certain methods were not favorably rated by radiologists. In engaging a specialized group of ten radiologists, this study presented them with MRI images annotated with different explanation techniques alongside unannotated images to establish a clear comparison and gauge the added value of these explanation methods. DeepLIFT stood out significantly in this study, praised for enhancing the radiologists' ability to detect lesions accurately. It offered not only high-quality insights but also provided visualizations that were clear and directly applicable to the diagnostic process, factors that contributed to its top utility rating. On the other end of the spectrum, SHAP and other lower-rated methods struggled to gain approval from the radiologists, primarily due to their failure to present clear, actionable insights that could be seamlessly integrated into the diagnostic workflow. In delving deeper into uncertainty-based explanation methods, this study revealed a nuanced landscape of preferences among radiologists.