# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline  & & White-box models are widely used in the medical field because of their high interpretability despite their low performance. Explainability of back-box models is needed to enable the use of more effective ML models in the medical domain. \\ \hline  & & The authors mentioned that explainability is important in their specific application to provide insights into the model’s behavior and facilitate the interpretation of the relationships between input parameters and predictions. Further, they mentioned that this way enables a better understanding of the model’s decision-making process and enhances the trustworthiness of the predictions, which is essential for optimizing battery management systems and extending battery life. \\ \hline  & & Ambient particle matter forecast experts tend to question the reliability of ML models and validity of their predictions. Explainability is needed to increase trust towards black-box models. \\ \hline  & & The authors mentioned that explainability is important in their specific application to align the decision-making process with that of human radiologists and to provide clear, human-readable justifications for model decision-making. Further, they mentioned explainability is essential for gaining trust in the model’s decisions and ensuring its reliability in the medical imaging domain. \\ \hline  & & The proposed ensemble method for malware prediction is quite complex, and explainability is needed to enable interpretation and validation of model-made decisions. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model's accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model's accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & & The authors mentioned that explainability is important in their specific application to understand and interpret the results of machine learning and deep learning models applied to lithium-ion battery datasets. Further, they mentioned that using XAI researchers can gain insights into the outcomes produced by the algorithms, describe the model’s accuracy, fairness, transparency, and results in decision-making, and investigate any biases in predicted results. \\ \hline  & & & \\ \hline \end{tabular}
\end{table}