# 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 Vo et al. (2023) [420] & Dragon-fruit ripeness (from images) & \begin{tabular}{p{113.8 pt}} To explain the outcomes of the image classification model and thereby enhance its performance, optimization, and reliability. \\ \end{tabular} \\ \hline Artelt et al. (2022) [421] & NA & There is no specific application. \\ \hline Abeyathna et al. (2021) [422] & NA & There is no specific application. \\ \hline Krenn et al. (2021) [276] & Experimental quantum optics & \begin{tabular}{p{113.8 pt}} The interpretable representation and enormous speed-up allow one to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. \\ \end{tabular} \\ \hline Pandiyan et al. (2023) [423] & Laser powder bed fusion process & \begin{tabular}{p{113.8 pt}} To highlight the most relevant parts of the input data for making a prediction. \\ \end{tabular} \\ \hline Huang et al. (2023) [222] & Assessment of familiarity ratings for domain concepts & \begin{tabular}{p{113.8 pt}} To be able to evaluate familiarity ratings of domain concepts more in-depth and to underline the importance of focusing on domain concepts’ familiarity ratings to pinpoint helpful linguistic predictors for assessing students’ cognitive engagement during language learning or online discussions. \\ \end{tabular} \\ \hline Jeon et al. (2023) [424] & Land use (from satellite images) & \begin{tabular}{p{113.8 pt}} To enhance the reliability of the image analysis. \\ \end{tabular} \\ \hline Fernandez et al. (2022) [264] & NA & There is no specific application; however, the explainability is important because of the increasing number of applications where it is advisable and even compulsory to provide an explanation. \\ \hline Jia et al. (2022) [425] & WiFi fingerprint-based localization & \begin{tabular}{p{113.8 pt}} To improve the trust of the proposed method. \\ \end{tabular} \\ \hline Munkhdalai et al. (2022) [426] & NA & There is no specific application. \\ \hline Schrills et al. (2023) [295] & Subjective information processing awareness (in automated insulin delivery) & \begin{tabular}{p{113.8 pt}} To help users cooperate with AI systems by addressing the challenge of opacity, subjective information processing awareness (SIPA) is strongly correlated with trust and satisfaction with explanations; therefore, explanations and higher levels of transparency may improve cooperation between humans and intelligent systems. \\ \end{tabular} \\ \hline Gouabou et al. (2022) [427] & Melanoma detection & \
\begin{tabular}{p{113.8 pt}} To overcome the dermatologist's fear of being misled by a false negative and the assimilation of CNNs to a \"black box,\" making their decision process difficult to understand by a non-expert. \\ \end{tabular} \\ \hline Okazaki et al. (2022) [205] &
\begin{tabular}{p{113.8 pt}} Customer journey mapping automation (through model-level data fusion) \\ \end{tabular} \\ \hline \end{tabular}   
\begin{table}
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**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline \multirow{6}{*}{Mridha et al. (2023) [41]} & & Using provided explanations, the clinician may notice the color irregularity in the dermatoscopic picture, which is not evident on the lesion, and figure out why the classifier predicted incorrectly. \\ \cline{1-1} \cline{2-3}  & Abeyrathna et al. (2021) [428] & NA & There is no specific application. \\ \cline{1-1} \cline{2-3}  & & The Grad-CAM method has been used for the authors to be sure that their method has used only the pixels from certain locations (where lungs are) of the image used for classification; the explanation itself is not important here. \\ \cline{1-1} \cline{2-3}  & & Misinformation detection (specifically COVID-19 misinformation; from texts) & Knowing the reasoning behind the outcomes is essential to making the detector trustworthy.