# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline Quach et al. (2023) [141] & Disease detection in agriculture & The authors mentioned that the explainability in their context is important because it is essential for making in-depth assessments and ensuring reliability in practice. & Productivity in production is a complex situation and is hard to model with linear ML models. More complex models would perform better, but with the cost of interpretability. & The authors mentioned that the explainability is essential here because it gives the ability to understand why a tweet has been classified as xenophobic. They mentioned that tweets can affect people’s behavior, and the development of an XAI model is essential to providing a set of explainable patterns describing xenophobic posts. This method can enable experts in the application area to analyze and predict xenophobic trends effectively. & The authors mentioned that explainability is essential in this context because the level of automation is constantly increasing according to the development of AI. And also, they mentioned ADAS assists drivers in driving functions, and it is essential to know the reasons for the decisions taken. And also, they said trusted AI is the cornerstone of the confidence needed in this research area. & When using AI-based systems for decision-making in healthcare, it is important for patient health that the model is interpretable and that practitioners can justify the model’s decisions. Both clinical healthcare practitioners and patients need to trust the AI system when AI is used for diagnostic decision-making. & The authors mentioned that the IMC features are difficult to interpret and control independently without affecting other features, and therefore the quality differences cannot be regarded as the sole response due to the change of a specific feature. To address this issue, the explainability requires and interprets the relationship between the features in the IMC and each part quality. \\ \hline \end{tabular}
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Table **A1**: _Cont._  
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline \multirow{9}{*}{Varghese et al. (2023)[104]} & & The authors mentioned that explainability provides insights into the features or characteristics of the model used to make predictions in the context of AD classification. And also, they mentioned it is more important for improving trust in the system and its results. \\ \cline{2-3}  & Heat transfer optimization for nanoporous coated surfaces & The authors mentioned that explainability uncovers the most influencing surface features for the nanoporous coatings. \\ \cline{2-3}  & & The authors mentioned that it is essential to understand how the model makes decisions and to ensure the model’s predictions are not based on biased features. \\ \cline{2-3}  & & The authors mentioned that explainability in their specific application is important to increase the transparency of the model to improve usability. And also, they mentioned it is important to improve the decision-making process and to understand the factors influencing the semiconductor manufacturing field. \\ \cline{2-3}  & & The authors mentioned that explainability is important in their context because it extends the interpretability of ML models and makes the results more understandable to humans. Also, they mentioned it is important to uncover how runoff routing is being resolved and to run black box models into glass box models. \\ \cline{2-3}  & & Global explanations are used to explain the model as a whole, but those explanations don’t provide exact information of where important features are. More research is needed to create explanation methods that give both global and local explanations, because both of those methods separately have severe limitations.