# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
; writing--review and editing, M.S. and V.P. All authors have read and agreed to the published version of the manuscript. The work by M.S. was supported by the K.H. Renlund Foundation and the Academy of Finland (project no. 356314). The work by V.P. was supported by the Slovenian Research Agency (Research Core Funding No. P2-0057). Not applicable. The review was not registered; however, the dataset created during the full-text review, including predefined codes and protocol details, is available from the first author upon request. The authors (M.S. and V.P.) would like to thank Lilia Georgieva for serving with them as a guest editor of the special issue on \"Recent Application of XAI\" that initiated this review. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ALE & Accumulated Local Effects \\ AI & Artificial Intelligence \\ CAM & Class Activation Mapping \\ COVID-19 & Coronavirus Disease \\ CYP & Cytochrome P450 \\ DT & Decision Tree \\ ICU & Intensive Care Unit \\ IEEE & Institute of Electrical and Electronics Engineers \\ IG & Integrated Gradients \\ IML & Interpretable Machine Learning \\ IoT & Internet of Things \\ k-NN & k-Nearest Neighbor \\ LIME & Local Interpretable Model-agnostic Explanations \\ LR & Logistic Regression \\ LRP & Layer-wise Relevance Propagation \\ ML & Machine Learning \\ NN & Neural Network \\ PDP & Partial Dependency Plots \\ PRISMA & Preferred Reporting Items for Systematic Reviews and Meta-Analysis \\ RISE & Randomized Input Sampling for Explanation \\ SHAP & SHapley Additive exPlanations \\ SVM & Support Vector Machine \\ WoS & Web of Science \\ XAI & Explainable Artificial Intelligence \\ \end{tabular} \begin{table}
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline Alshehri et al. (2023) [131] & Groundwater quality monitoring & Explainability techniques provide valuable insights on ML-made decisions, which is valuable for decision-making in water quality management. When important features are known, decisions can be made to focus on getting them better first. & Mistakes made by self-driving cars can lead to dangerous accidents. Explainability gives insight into why models make mistakes and, therefore, leads to better development and safer cars. & Explanations on model predictions help users understand significant features that the LSTM model predicts credit card fraud with. \\ \hline  & & The authors highlighted the explainability of deepfake voice detection to ensure the system’s reliability and trustworthiness by allowing users to understand and trust its decisions. They aimed to deliver interpretations at a human perception level, making the results comprehensible for non-experts. This approach differentiates human and deepfake voices, improving the system’s effectiveness. & Applying explainability methods in ML models used in the healthcare field is important because otherwise practitioners cannot understand the reasons behind decisions made by ML models. \\ \hline  & & Earthquakes can lead to significant financial losses and casualties, and that is why ML models for earthquake prediction are developed. Because ML models get more complex, explainability is needed to interpret the results and to design better models. \\ \hline  & & In system prognostics and health management, explainability is needed to increase the reliability of decisions made by remaining useful lifetime prediction models and also to gain knowledge on what parts caused the engine to fail. Increasing the reliability of remaining useful lifetime models is important because too early maintenance is a useless cost, and too late maintenance results in unexpected downtime, which is also a useless cost. \\ \hline  & & In healthcare, doctors need explanations of the ML model’s decisions so they can make the right decisions in patient care. \\ \hline  & & \\ \end{tabular}
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