## 3 Results
### XAI Advantages and Disadvantages  
Comparison of XAI methods reveals relative advantages and disadvantages across visualization, semantics, and example-based functions, highlighting the varied clinical relevance and potential impact of each method. Visualization methods such as CAM and Grad-CAM provide direct and intuitive visual insights into influential areas within an image, particularly useful for tumor detection or classification tasks. These methods preserve high-resolution spatial information and are class-discriminative, making them suitable for differentiating between various conditions across a wide range of CNN architectures, without the need for architectural modifications. However, their requirement of a CNN architecture (with global average pooling) limits their broader application. This limited choice of algorithms could potentially exclude (neural) networks that could better fit the data. Additionally, saliency-based methods like Grad-CAM and LIME often rely on external validation to verify their accuracy. LIME uses simple models for local approximations, making its explanations easily understandable. The perturbation of the input shows the resultant change in the model output and can be used to explain which regions are driving the predictions directly. Despite being model-agnostic and providing specific, locally focused explanations, LIME may not generalize across different datasets. The method's reliability heavily depends on how perturbations are implemented, and managing feature contributions in high-dimensional data can be challenging, necessitating careful interpretation to prevent misleading outcomes [42]. Clinically, visualization XAI methods could be implemented in US for tumor detection in various cancer types. This could result in a more thorough evaluation of the US images. Semantic methods in XAI aim to bridge the gap between complex algorithmic image analysis and practical clinical interpretation by incorporating clinically relevant lexicons for higher confidence in class decisions. The bag of features (BoF) method concentrates on well-defined, localized features for detailed US image analysis. In [PERSON] et al. (2023), BoF was used to identify features unknown to radiologists lexicons, but present in 78% of tumor-labeled cases. This feature could be added to the lexicon after its characterization is determined [38]. Other semantic methods, such as BI-RADS-Net, utilize BI-RADS descriptors to align class predictions more with clinical practice, effectively integrating the explanations into clinical workflows [52]. MT-BI-RADS expands upon this by providing both visual and semantic explanations of the significance of each BI-RADS descriptor in thedecision-making processes. The Shapley values offer quantifiable insight into individual feature importance, explaining complex patterns in histopathological images and aiding clinical decision making [43]. Despite these advantages, the semantic methods identified were mainly developed for classification tasks, which could hinder their applications for segmentation or localization. Additionally, while methods like LTQ-E are effective in pattern extraction for TI-RADS labels at the image level, their complexity may potentially limit routine clinical use [46]. Semantic methods could be used to explain the AI models' way of interpreting the difference between malignant and benign tumors in US images. Example-based methods provide substantial educational and practical advantages in AI model validation and debugging by aligning model reasoning with clinical expert judgment. These methods are particularly beneficial in reducing human subjectivity, as demonstrated by the AIBx image similarity in [PERSON] et al. (2020), which reduced the number of unnecessary biopsies in clinical studies compared to decisions made without similarity support [51]. The task-specific design and use of familiar diagnostic categories for example-based XAI enhance user trust and integration into clinical systems.