# Evaluating Explainable Artificial Intelligence for X-ray Image Analysis
## 3 Results and Discussion
### RQ6: Post Hoc or Model-Based?  
The work of [17] introduced a categorization of existing explainable techniques into model-based and post hoc categories. A model-based technique is defined as the construction of models that readily provide insight into the relationships they have learned. The main challenge of model-based explainability is to come up with models that are simple enough to be easily understood by the audience yet sophisticated enough to properly fit the underlying data. A post hoc technique is defined as the analysis of a trained model in order to provide insights into the learned relationships. When there is an interest in more general relationships learned by a model, e.g., relationships that are relevant for a particular class of responses or subpopulation, they use dataset-level explainability.  
Figure 13 shows the different types of methods detected in the answers to RQ6. We can see that most of the studies, 70.92%, use post hoc techniques, while 25.53% use model-based methods. This difference is caused by the greater simplicity of the post hoc approach.  
Figure 12: Heat maps comparing the results of RQ2 and RQ5. All articles that did not indicate the type of XAI method used are shown. (**a**) In absolute values; (**b**) normalized results by column.  
Figure 13: Type of method used, obtained from the answers to RQ6. Vertical axis indicates the percentage of articles for each category.  
Finally, we found a set of methods (3.55%) that combined both approaches. To combine them, most articles used the results of the post hoc method as an input for the transparent model to gain explainability as a whole.  
Figure 14a,b allowed us to check which XAI method is considered post hoc or model-based. We compared the results of RQ6 with those obtained with RQ5. Most of the algorithm is only in one category; for example, all techniques of features visualization were always considered as post hoc models, but some algorithms, even if they were a post hoc model, could be used as a foundation for model-based techniques. Accordingly, 4.17% of the studies that used CAM methods integrate them into models and, for this reason, are categorized as model-based. The results of this comparison were expected, as the XAI method is the element that determines if a method is post hoc or model-based.  
Taking into consideration the results obtained with RQ1 and comparing them with those obtained with this question, as shown in Figure 15a,b, we can see that the model-based methods are predominantly used in the studies centered on thoracic problems. This can be explained by the availability of comparatively large and high-quality datasets with thoracic images, which allowed the use of more complex methods such as GANs or methods for text generation. These results are also compatible with, and similar to, those obtained to compare RQ1 and RQ5. The cause of this similarity is discussed in the previous paragraph.  
Figure 14: Heat maps comparing the results of RQ5 and RQ6. All articles that did not indicate which type of XAI method used are shown. (**a**) In absolute values; (**b**) normalized results by column, values in percentages.  
The larger use of post hoc models was caused by their inherent simplicity in comparison to model-based approaches. The usage of model-based models requires more extensive knowledge of the algorithm to accomplish the goal of both transparency and good performance. In contrast, post hoc methods are simpler to use and require less expertise, and can even be agnostic, meaning that they did not depend on the underlying method, such as LIME.