# Evaluating Explainable Artificial Intelligence for X-ray Image Analysis
## 1 Introduction  
Artificial intelligence (AI) is used in many fields, and this usage has increased with the appearance of deep learning models. These models have achieved impressive results, but these results are frequently difficult to understand for the human user. To overcome the limitation of these models, also named black-box models, explainable artificial intelligence (XAI) emerged to be able to understand, trust, and effectively manage this emerging generation of artificially intelligent partners [1]. XAI approaches were proposed to make a shift towards more transparent AI, aiming to create a suite of techniques that produce more explainable models whilst maintaining high-performance levels [2]. Even with the increased importance of XAI, some basic questions remain without a consensus, including what explainable artificial intelligence is. In this paper, we used the definition proposed in [3] that defined it as follows: _Given an audience, an explainable artificial intelligence is one that produces details or reasons to make its functioning clear or easy to understand, and trust the behavior of intelligent systems_, as users can verify the reasons for the computer's presentations or assertions as true and can understand them, and, furthermore, as assessed in [4], anticipate circumstances in which the machine's recommendations will not be trustworthy, in which case the computer's recommendations should not be followed even though they appear trustworthy. The appearance of XAI approaches is due to the dangers of the black-box models. Making decisions based on these models produced ethical and technical concerns caused by the fact that these models were not understood and, consequently, their decisions were not justified. Solving these dangers has an importance that depends on the field of study to be applied. In some of these fields, e.g., medicine, fixing these problems is vital. For verification of the results and ethical and legal issues, the non-explained prediction has not been sufficient to support a diagnosis and therefore has not been suitable for medical practice. XAI algorithms overcome the caveats of black-box models, increasing their explainability. Most of these methods cannot be used without some model working as a backbone. The algorithms are typically used alongside black-box models and aim to improve them, but they are also fully independent from one other. The main concern of any study about explainability should be XAI algorithms and not the AI models. We found multiple solutions aiming to do so. For example, one of the biggest fields of study is saliency methods, such as class activation maps (CAMs) [5] or attention, characterized by indicating the importance of each pixel for the task to be explained; however, we can also find completely different methods, such as those aiming to obtain textual explanations through auxiliary models. This diversity within explainability algorithms makes some kind of classification necessary to simplify further decision-making processes. In the medical images field, the interest in radiography analysis has provoked a significant increase in requests for the analysis of X-ray images. The usage of AI to support radiological work is a promising field of study, as indicated in several recent reviews about the topic [6; 7; 8]. One of the problems of applying XAI to the analysis of X-ray images is a different kind of image that includes radiography analysis to support the diagnosis of multiple diseases, such as pneumonia [9; 10] and bone diseases [11; 12; 13], among others. This trend was augmented with the appearance of COVID-19, as could be seen in the publication of several guidelines to perform this analysis, such as the one proposed by the Spanish Society of Emergency Radiology (SERAU) that had standardized the radiological report in the case of COVID-19 infection ([[http://serau.org/covid-19/](http://serau.org/covid-19/)]([http://serau.org/covid-19/](http://serau.org/covid-19/)) (accessed on 25 April 2022)).