# Evaluating Explainable Artificial Intelligence for X-ray Image Analysis
## 3 Results and Discussion
### RQ3: What Data Are Used? The answers to this question allowed us to observe the large number of datasets used by the reviewed studies. First, we found that 18.43% of the articles did not indicate which data they used, or the data were not publicly available. The rest of the studies used 26 different datasets, as can be seen in Figure 9. Most of the articles used two or more of these datasets. We grouped the datasets into five categories depending on the goal of the dataset: pulmonary diseases, COVID-19, tuberculosis-related disease, bone-related diseases, and dental images. We distinguished COVID-19 and tuberculosis from the rest of the pulmonary diseases due to their relevance and the amount of interest in both diseases in the reviewed studies. Figure 8: Heat maps comparing the results of RQ1 and RQ2. (**a**) In absolute values; (**b**) normalizing the results by column. The reviewed articles used 10 different datasets for COVID-19 detection. Twenty-six (22.61%) articles used the dataset proposed in [30], which provides a set of X-ray images of multiple diseases, including COVID-19. This dataset was built using data from multiple previously published datasets. The dataset from [33] was used in eight studies (6.12%). The authors in [34] published another COVID-19 dataset, the COVIDx. This dataset was used in eight articles (6.12%). Five studies (4.35%) used images available at [35], a public repository for X-ray images. The authors in [31] published a dataset containing more than 20,000 samples of X-ray images. Four articles used [PERSON] et al. 's dataset. Two studies used the BIMCV COVID-19 dataset, obtained in the Valencian region of Spain [36]. Three datasets were used by only one study: the COVID-19 dataset [37], from The Cancer Imaging Archive (TCIA), the dataset from [38], and the dataset from the 3 rd Deep Learning and Artificial Intelligence Summer/Winter School (DLAIS), made available at Kaggle [39]. The studies that aim to detect tuberculosis used multiple datasets. The Montgomery County X-ray Set and the Shenzhen Hospital X-ray Set are both presented in [40] and contain X-ray images of patients with tuberculosis. Six studies used one or both of these datasets. Two studies used the Belarus dataset. One study also used the dataset published in [32]. We found eight different datasets for the detection of multiple pulmonary diseases. Fifty-eight articles (50.43% of the articles) used the ChestX-ray8 dataset. This dataset was firstly introduced in [41] and consists of 112,120 chest X-ray images. The authors in [42] proposed the CheXpert dataset, consisting of chest X-rays of healthy patients and patients with pulmonary diseases. The dataset was built with 224,316 chest radiographies. Fourteen studies used this dataset, representing 12.17% of the reviewed works. Thirteen studies (11.3%) used the dataset proposed in [28]. This dataset contains two kinds of chest X-ray images and optical computerized tomographies (OCTs). IU (Indiana University) X-ray images, also referred to as the Open-i dataset, are provided in [43], and eight studies used them (6.96%). Five studies used the data provided in [44], known as the MINIC-CXR collection. The main feature of this dataset is the combination of X-ray images with their respective radiology reports. One study used the pneumothorax dataset published in [45], and another one used the dataset published in [46], which contains two kinds of X-ray images with or without lung nodules. Only one study used the dataset proposed in [29]. This dataset is a combination of the JSRT, the Montgomery County set, and a set of images from India containing images of healthy and ill patients. Figure 9: Datasets used by the reviewed articles. Answers to RQ3. Vertical axis indicates the percentage of articles for each category. [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [30], [PERSON] et al. [31] and [PERSON] et al. [32].