# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
\\ \cline{2-3}  & & To assist/help novice dental clinicians (dental students) in decision-making. \\ \cline{2-3}  & & To give insights into the mechanisms that may limit mosquito breeding and disease transmission. \\ \cline{2-3}  & & To reduce a high rate of false alarms in cardiac arrest prediction models and to make their results clinically (more) interpretable. \begin{table}
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline Wen et al. (2023) [386] & Alzheimer disease detection (from patient transcriptions) & To discover the underlying relationships between PoS features and AD. \\ \hline Alvey et al. (2023) [387] & Aerial images analysis & To understand and explain the behavior of deep learning models. \\ \hline Maaroof et al. (2023) [82] & Diabetes prediction & To gain insight into how the model makes its predictions and build trust in its decision-making process. \\ \hline Hou et al. (2022) [388] & Image classification & To produce improved filters for preventing advanced backdoor attacks. \\ \hline Nakagawa et al. (2021) [389] & Mortality prediction of COVID-19 patients (from healthcare data) & To allow data scientists and developers to have a holistic view, a better understanding of the explainable machine learning process, and to build trust. \\ \hline Yang et al. (2022) [390] & Process execution time prediction & To explain how ML models predicting the time until the next activity in the manufacturing process works. \\ \hline O’Shea et al. (2023) [391] & Lung tumor detection & Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. \\ \hline Tasnim et al. (2023) [392] & Cardiology & To examine the contribution of features to the decision-making process and to foster public confidence and trust in ML model predictions. \\ \hline Marques-Silva et al. (2023) [393] & NA & NA \\ \hline Lin et al. (2023) [274] & Visual reasoning & To disclose/explain the decision-making process from the numerous parameters and complex non-linear functions. \\ \hline Pedraza et al. (2023) [394] & Sensor measurements & To better understand the AI model. \\ \hline Kwon et al. (2023) [395] & NA & To derive a mechanism of quantifying the importance of words from the explainability score of each word in the text. \\ \hline Rosenberg et al. (2023) [396] & Integer linear programming and quadratic unconstrained binary optimization & Explainability is needed so that ML models can be trusted. Explainability can also help detect biases and help improve the ML model. Expressive boolean formulas for explanations can increase flexibility and interpretability. \\ \hline O’Sullivan et al. (2023) [397] & Water quality modeling & Neural network models provide good performance in simulating water quality, but because of bad explainability, the model’s decisions are hard to use to make management decisions. Explainability would increase trust and usability of ANN models in water quality modeling. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. \begin{table}
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline \hline \multirow{9}{*}{Kalutharage et al. (2023)[406]} & \multirow{9}{*}{Anomaly detection} & Explainability is important in AI applications in the field of cybersecurity because one mistake made by the model can lead to a large amount of damage. Explanations make the ML model trustworthy and also enable better model development. Detecting important features can also increase the performance of the intrusion detection system. \\ \hline \hline \multirow{9}{*}{Sorayaie Azar et al. (2023)[407]} & \multirow{9}{*}{Monkeypox detection} & The authors mentioned that the explainability is important in their specific application to provide a clear understanding of the decision-making process of the AI models.