# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 1 Introduction  
In recent decades, there has been a rapid surge in the development and widespread utilization of artificial intelligence (AI) and Machine Learning (ML). The complexity and scale of these models have expanded in pursuit of improved predictive capabilities. However, there is growing scrutiny directed towards the sole emphasis on model performance. This approach often results in the creation of opaque, large-scale models, making it challenging for users to assess, comprehend, and potentially rectify the system's decisions. Consequently, there is a pressing need for interpretable and explainable AI (XAI), which aims to enhance the comprehensibility of AI systems and their outputs for humans. The advent of deep learning over the past decade has intensified efforts to devise methodologies for elucidating and interpreting these opaque systems [1; 2; 3].  
The literature on XAI is highly diverse, spanning multiple (sub-)disciplines [4], and has been growing at an exponential rate [5]. While numerous reviews have been publishedon XAI in general [1; 2; 5], there is a noticeable gap when it comes to in-depth analyses focused specifically on XAI applications. Existing reviews predominantly explore foundational concepts and theoretical advancements, but only a few concentrate on how XAI is being applied across different domains. Although a few reviews on XAI applications do exist [6; 7; 8], they have limitations in terms of the coverage period and the number of articles reviewed. For instance, Hu et al. [6] published their review in 2021, thus excluding any articles published thereafter. Additionally, they do not specify the total number of articles reviewed, and their reference list includes only 70 articles. Similarly, Islam et al. [7] and Saranya and Subhashini [8] reviewed 137 and 91 articles, respectively, but also focused on earlier periods, leaving a gap in the literature regarding the latest XAI applications.  
In contrast, our review fills this gap by providing a more comprehensive and up-to-date synthesis of XAI applications, analyzing a significantly larger set of 512 recent articles. Each article was thoroughly reviewed and categorized according to predefined codes, enabling a systematic and detailed examination of current trends and developments in XAI applications. This broader scope not only captures the latest advancements but also offers a more thorough and nuanced overview than previous reviews, making it a valuable resource for understanding the current landscape of XAI applications.  
Given the rapid advancements and diverse applications of XAI, our research focuses on addressing the following key questions:  
* **Domains**: what are the most common domains of recent XAI applications, and what are emerging XAI domains?
* **Techniques**: Which XAI techniques are utilized? How do these techniques vary based on the type of data used, and in what forms are the explanations presented?
* **Evaluation**: How is explainability measured? Are specific metrics or evaluation methods employed?  
The remainder of this review is structured as follows: In Section 2, we provide a brief overview of XAI taxonomies. Section 3 details the process used to identify relevant recent XAI application articles, along with our coding and review procedures. Section 4 presents the findings, highlighting the most common and emerging XAI application domains, the techniques employed based on data type, and a summary of how the different XAI explanations were evaluated. Finally, in Section 5, we discuss our findings in the context of our research questions and suggest directions for future research.