# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions  
This systematic literature review explored recent applications of Explainable AI (XAI) over the last three years, identifying 664 relevant articles from the Web of Science (WoS). After applying exclusion criteria, 512 articles were categorized based on their application  
Figure 11: Evaluation of the explanations in recent XAI application papers. domains, utilized techniques, and evaluation methods. The findings indicate a dominant trend in health-related applications, particularly in cancer prediction and diagnosis, COVID-19 management, and various other medical imaging and diagnostic uses. Other significant domains include environmental and agricultural applications, urban and industrial optimization, manufacturing, security and defense, finance, transportation, education, psychology, social care, law, natural language processing, and entertainment. In health, XAI has been extensively applied to areas such as cancer detection, brain and neuroscience studies, and general healthcare management. Environmental applications span earthquake prediction, water resources management, and climate analysis. Urban and industrial applications focus on energy performance, waste treatment, and manufacturing processes. In security, XAI techniques enhance cybersecurity and intrusion detection. Financial applications improve decision-making processes in banking and asset management. Transportation studies leverage XAI for autonomous vehicles and marine navigation. The review also highlights emerging XAI applications in education for predicting student performance and in social care for child welfare screening. In categorizing recent XAI applications, we aimed to identify and highlight the most significant overarching themes within the literature. While some categories, such as \"health\", are clearly defined and widely recognized within the research community, others, like \"in-dustry\" and \"technology\", are broader and less distinct. The latter categories encompass a diverse range of applications, reflecting the varied contexts in which XAI methods are employed across different sectors. This categorization approach, though occasionally less precise, captures the most critical global trends in XAI research. It acknowledges the interdisciplinary nature of the field, where specific categories may overlap or lack the specificity found in others. Despite these challenges, our goal was to provide a comprehensive overview that highlights the most prominent domains where XAI is being applied while recognizing that some categories, by their nature, are more general and encompass a wider array of subfields. By far the most frequent ML task among the reviewed XAI papers is classification, followed by regression and clustering. Among the used ML models, deep neural networks are predominant, especially convolutional neural networks. The second most used group of ML models are tree-based models (decision and regression trees, random forest, and other types of tree ensembles). Interestingly, there is no substantial difference between the major ML models with regard to the ML task of their target application. Feature importance, referring to techniques that assign a score to input features based on how useful they are at predicting a target variable [26], is the most common form of explanation among the reviewed XAI papers. Some sort of visualization, trying to visually represent the (hidden) knowledge of a ML model [301], is used very often as well. Other commonly used forms of explanation include the use of saliency maps, rules, and counterfactuals. Regarding methods, local explanations are predominant, with SHAP and LIME being the most commonly used techniques. SHAP is preferred for its stability and mathematical guarantees [240], while LIME is noted for its model-agnostic nature but criticized for its instability [32]. Gradient-based techniques such as Grad-CAM, Grad-CAM++, SmoothGrad, LRP, and Integrated Gradients are frequently used for image and complex data [179; 182].