# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline  & & The authors mentioned that explainability in their specific application is important for building trust and confidence in the model's decisions and to know why the system has made a particular decision. Further, they mentioned that using explanations for the model's decisions increases trust among users and also potentially teaches humans to make better decisions in skin lesion classification. \\ \hline  & & The authors mentioned that in the field of energy management, understanding complex AI models can be challenging because of the black box nature. And they mentioned that using explainability can explain the impact of input variables on the model's output. Further, they mentioned that explainability is essential for EMS managers to comprehend why specific predictions are made, enabling informed decision-making in energy management processes. \\ \hline  & & The authors mentioned that the explainability of their specific application is important to improve the interpretability of the deep learning model's outputs, which is essential for medical professionals to trust and understand the diagnostic results, and it is leading to faster diagnosis and early treatment. \\ \hline  & & The authors mentioned that explainability is important in their specific application specific application to build trust, enhance understanding, improve decision-making processes, and manage uncertainty in the context of psychiatric care and mental health diagnosis. \\ \hline  & & The authors mentioned that explainability in their specific application is important to address the challenges of interpreting heterogeneous data and to provide reliable explanations for the models used in cybersecurity applications, especially in malware detection. \\ \end{tabular}   
## References  
* Adadi and Berrada (2018) Adadi, A.; Berrada, M. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). _IEEE Access_**2018**, \(6\), 52138-52160. [CrossRef]
* Minh et al. (2022) Minh, D.; Wang, H.X. ; Li, Y.F. ; Nguyen, T.N. Explainable artificial intelligence: A comprehensive review. _Artif. Intell. Rev._**2022**, _55_, 3503-3568. [CrossRef]
* Saeed and Ohmi (2023) Saeed, W.; Ohmi, C. Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. _Knowl.-Based Syst._**2023**, _263_, 110273. [CrossRef]
* Nauta et al. (2023) Nauta, M.; Trienes, J.; Pathak, S.; Nguyen, E.; Peters, M.; Schmitt, Y.; Schlotterer, J.; van Keulen, M.; Seifert, C. From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai. _ACM Comput. Surv._**2023**, _55_, 295. [CrossRef]Arrieta, A.B. ; Diaz-Rodriguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; Garcia, S.; Gil-Lopez, S.; Molina, D.; Benjamins, R.; et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. _Inf. Fusion_**2020**, _58_, 82-115. [CrossRef]
* Hu et al. (2021) Hu, Z.F. ; Kuflik, T.; Mocanu, I.G. ; Najafian, S.; Shulner Tal, A. Recent studies of xai-review. In Proceedings of the Adjunct 29 th ACM Conference on User Modeling, Adaptation and Personalization, Utrecht, The Netherlands, 21-25 June 2021; pp. 421-431. Islam et al. (2022) Islam, M.R. ; Ahmed, M.U. ; Barua, S.; Begum, S. A systematic review of explainable artificial intelligence in terms of different application domains and tasks. _Appl. Sci._**2022**, _12_, 1353. [CrossRef]
* Saranya and Subhashini (2023) Saranya, A.; Subhashini, R. A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends. _Decis. Anal. J._**2023**, \(7\), 100230. * Schwalbe and Finzel (2024) Schwalbe, G.; Finzel, B. A comprehensive taxonomy for explainable artificial intelligence: A systematic survey of surveys on methods and concepts. _Data Min. Knowl. Discov._**2024**, _38_, 3043-3101.