# Evaluating Explainable Artificial Intelligence for X-ray Image Analysis
## 3 Results and Discussion
### RQ7: What Kind of Explainability Is Obtained? This question aims to classify the reviewed studies depending on their respective output. These outputs represented a different type of explainability, independently of the underlying technique used. Distinct techniques could generate similar outputs. Figure 16 indicates the results obtained from this research question. The authors in [3] proposed a taxonomy for the different types of explainability. Based on this taxonomy, we identified three different types of explainability used for X-ray images:  
* Visual explanations. Techniques that aim to visualize the model behavior. In this category, we include dimensional reduction techniques and saliency maps. The techniques of dimension reduction, such as t-SNE (introduced in [62]), allowed for human interpretations via the simplification of the handled data. Saliency maps were first introduced in [63], aiming to visualize and identify the significant visual features from an image for a model of artificial intelligence. * Text explanations. A set of techniques based on text generation algorithms to obtain explanations in natural language. * Explanations by example. Based on case-based reasoning (CBR), these are characterized by the search for a previously known image to explain the decision made by artificial intelligence. The findings of this question are highly related to those obtained with RQ5. Each of the techniques used in the reviewed studies used one of the methods detected with RQ5. We found that the kind of explainability most often used is a visual explanation. In particular, we found that 85.81% of the articles generated a saliency map. The predominance of this technique was expected, taking into consideration that we reviewed articles that analyzed X-ray images and that the technique was specially designed for image analysis. Figure 15: Heat maps comparing the results of RQ1 and RQ6. (**a**) In absolute values; (**b**) normalized results by column. Text explanations were the second most common approach for explainability, used for 5.67% of the studies, and 2.12% of the articles were based on techniques of explanation by example. One study proposed the creation of a numerical ratio to grade the probability of the presence of disease. The rest of the articles combined two or more of the previous techniques: saliency maps and dimensional reduction techniques (2.836%), text explanations and saliency maps (1.418%), saliency maps and explanations by example (0.709%), and disease ratios and saliency maps (0.709%). The results of this research question are highly related to those obtained in RQ1, RQ2, and RQ5. The relation between RQ5, RQ2, and RQ1 is discussed in previous sections; thus, it can be expected that similarities between them and RQ7 are found. We found, in the comparison between RQ1 and RQ6, that the vast majority of model-based methods were used for thoracic problems. This relation is also observable in the relation between the results of RQ7 and RQ1, as can be seen in Figure 17a,b. Figure 18a,b allowed us to check if there was some relation between the AI method used and the kind of explainability obtained. We detected two different elements: a predominance of saliency maps, without a meaningful difference between different AI methods, and a relation between some kind of explainability and the AI method; e.g., text explanations are almost only obtained with a combination of a CNN and an RNN. In Figure 19b, we can see that the majority of articles that indicated which XAI method was used generated saliency maps. By contrast, in Figure 19a, it is clear that all articles that did not indicate the XAI algorithm or that used model-based approximations generated a vastly more diverse set of algorithms. Figure 16: Kind of explainability found by answering RQ7. Vertical axis indicates the percentage of articles for each category. Figure 17: Heat maps comparing the results of RQ1 and RQ7. (**a**) In absolute values; (**b**) normalized results by column, values in percentages.