# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
\begin{table}
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline \multirow{4}{*}{Laqua et al. (2023)[496]} & \multirow{4}{*}{E-bikes} & The authors mentioned that explainability is important in their specific application to enhance the understanding of the user experience of e-bike riding. \\ \cline{1-1}  & & The authors mentioned that explainability is important in their explainability is important in their predictability, validation, feature selection optimization, and advancing scientific discovery in the context of predicting antimicrobial mechanisms of action using AI models. \\ \hline \multirow{4}{*}{Sanderson et al. (2023)[497]} & \multirow{4}{*}{Flood inundation mapping} & The authors mentioned that deep learning models are often considered “black boxes”, which can have challenges regarding transparency and potential ethical biases. Using XAI to flood inundation mapping, they aimed to enhance insight into the behavior of their proposed deep learning model and how it is impacted by varying input data types. \\ \hline \multirow{4}{*}{Abe et al. (2023)[498]} & \multirow{4}{*}{Estimating pathogenicity of genetic variants} & The authors mentioned that by incorporating explainability into their AI model, they can provide understandable explanations to physicians and make informed decisions based on the AI’s estimation results and genomic medical knowledge. And also, they mentioned this approach eliminates the bottlenecks in genomic medicine by combining high accuracy with explainability and supporting the identification of disease-causing variants in patients. \\ \hline \multirow{4}{*}{Kerz et al. (2023)[499]} & \multirow{4}{*}{Mental health detection} & The authors mentioned that there is a growing need for explainable AI approaches in psychiatric diagnosis and prediction to ensure transparency in the decision-making process. \\ \cline{1-1}  & & The authors mentioned that explainability in their specific application is important to improve the reliability of AI-based systems by providing visual explanations of predictions made by black-box deep learning models. And also, they mentioned explainability helps in preventing critical errors, especially false negative errors in image selection, and by providing visual explanations, the system can be refined based on supervisor feedback, which can reduce the risk of misinterpretation or incorrect predictions. \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. **Table A1. **_Cont._  
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline  & & Explainability is necessary to enable the use of complex and high-performing models in predicting water quality, because domain experts usually are not familiar with AI. They need interpretable and clear explanations to evaluate and trust the model’s decisions. \\ \hline  & & Explainability is necessary to ensure users’ trust toward the ML model and to help users understand the ML model better. Different XAI methods perform differently (especially saliency map techniques), and therefore efficient XAI evaluation techniques are needed to help find the most accurate and interpretable XAI technique. \\ \hline  & & The ML model used in weather prediction does not give information about the contributions of different features. Explainability is needed to gain insight on model performance and therefore evaluate the model’s decisions. \\ \hline  & & A ML model often has to deal with unseen data in arrythmia classification task, and explainability is needed to evaluate model performance and decisions in these cases. Healthcare professionals tend not to trust AI-based diagnostic tools, and explainability would increase trust towards ML models and therefore enable use of AI diagnostic tools. In the healthcare domain, explainability is also necessary in an ethical and legal sense.