# Evaluating Explainable Artificial Intelligence for X-ray Image Analysis
## 3 Results and Discussion
### RQ2: What AI Methods Are Used?  
With this question, we wanted to determine which backbone algorithm is used in each study. We found nine different AI methods used in the reviewed articles.  
As we can see in Figure 7, 96.74% of the reviewed articles used multiple architectures of the convolutional neural network (CNN). We found that 77.30% of the articles used a standalone CNN. This kind of model is based on the proposal by [20]. These CNNs are characterized by the fact that they output a probability for each image to pertain to some predefined class. Moreover, 7.09% of the reviewed articles combined a CNN and a recurrent neural network (RNN). The CNN was designed to extract features from images, and the RNN was first introduced in [21] to handle data with time information. Both techniques were combined to generate text from the image. Another model used was the ensemble of multiple CNNs (2.84%). This approach consists of the combination of the output of multiple standalone CNNs. Further, 3.54% of the studies used a fully convolutional neural network (FCN), a special implementation of a CNN first introduced in [22] to develop a specialized algorithm for semantic segmentation. We also found that 2.13% of the articles used the method proposed in [23]. In this work, they presented a special kind of neural network called a siamese or twin neural network. These models are built upon two sub-neural networks, and the final result was obtained by a comparison of these two to find similarities between images. This allowed for a comparison between an unknown image with a known image. We found that 2.13% of the articles used multiple instance learning (MIL), a method formalized in [24], in combination with a CNN. We also found one study that used CNN multitasking, an AI method first proposed in [25] that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias.  
Figure 7: Answers to RQ2. Vertical axis indicates the percentage of articles for each category.  
We found six studies that did not use a CNN as a backbone algorithm to perform the prediction. We found four articles that used a generative adversarial network (GAN). This technique was firstly introduced in [26] to generate realistic images. Two studies used a graph neural network (GNN), first introduced in [27], that combined a neural network with graph theory.  
Taking into consideration the results obtained with RQ1 and comparing them with those obtained with this question, as shown in Figure 8a,b, we can assert that the purpose did not affect which AI method was used. We can see the predominance of the CNN in each purpose, with at least 75% of articles in each category using this technique. Furthermore, the value is even higher if we consider the different kinds of CNNs as the same AI methods. The predominance of this method is because it is specially designed to work with images without taking into consideration the type of image or the anatomical zone depicted in them.  
By answering this question, we found that all articles reviewed used deep learning methods. We did not find any method that combines traditional machine learning techniques with explainability. This was caused by the fact that the explainability concept was first defined to overcome the limitation of deep learning in black-box models.