# XAI-Based Clinical Decision Support Systems: A Systematic Review
## 4 Applications  
### Function of CDSSs  
The objective of CDSSs is to facilitate optimal decision-making for patient safety. Consequently, the role of CDSSs is to support the decision-making processes involved in diagnosis, treatment, and prescription, which are directly related to patient safety. Table 6 illustrates the functions of CDSS. Figure 5: XAI taxonomy. The first function of CDSSs is diagnostic support. CDSSs provide diagnostic information on suspected diseases collecting and monitoring figures showing the patient's \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Features**} & \multicolumn{6}{c}{**XAI Methods**} \\ \cline{3-8}  & & & **Post** & **Hoc** & **Global** & **Local** & **Model** & **Model** \\  & & & **Hoc** & **Global** & **Local** & **Specific** & **Agnostic** \\ \hline \multirow{3}{*}{CAM Based} & \multirow{3}{*}{GradCAM [166]} & Feature classification by treating the slope of the prediction over the activation map as a weight & & & & \\ \cline{3-8}  & & & & & & \\ \hline \multirow{6}{*}{Backpropagation} & \multirow{3}{*}{Gradient [167]} & Reflect the rate of output change as input changes & & & & & \\ \cline{2-8}  & Guided & Backpropagate non-negative input and output gradients & & & & & \\ \cline{2-8}  & GuidedGradCAM [168] & Compute the binwise product between the guided backpropagation and the CAM signal & & & & & \\ \cline{2-8}  & \multirow{3}{*}{DeepLift [169]} & Differentiate backpropagation rules from reference operations to account for prediction differences from extraneous informational & & & & \\ \cline{2-8}  & & & & & & \\ \cline{2-8}  & Integrated Gradients [170] & Approximate a gradient from a neural baseline input to a target input & & & & & \\ \hline \multirow{6}{*}{Backpropagation Based} & \multirow{3}{*}{GradientShap} & Approximate Shapley values by computing the expectation of a gradient & & & & \\ \cline{2-8}  & \multirow{3}{*}{Deconvolution Based} & Modify gradient calculation rules in ReLU functions instead of backpropagating non-negative input gradients & & & & & \\ \cline{2-8}  & & & & & & \\ \cline{2-8}  & \multirow{3}{*}{SmoothGrad} & Smoothed noisy gradient signals by averaging heatmaps over input and imputed neighbor samples & & & & & \\ \hline \multirow{6}{*}{Input Inference Based} & \multirow{3}{*}{Kernel Shap [77]} & Occlude a portion of the image with a sliding window and average the difference in output as a feature attribute & & & & \\ \cline{2-8}  & & Shapley Value Sampling [173] & Compute Shapley values over a subset of all possible feature combinations & & & & \\ \cline{2-8}  & \multirow{3}{*}{Kernel Shap [77]} & Use non-modality-specific heatmaps & & & & & \\ \cline{2-8}  & & Replace image features by shuffling feature values within a batch and compute the resulting prediction difference & & & & & \\ \cline{2-8}  & \multirow{3}{*}{LIME [68]} & Sampling neighboring data around the input to learn an interpretable model & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: XAI Models. conditions, such as biosignals. This function provides efficient management of beds by supporting useful decision-making to inexperienced clinicians or nursing personnel. The second function is treatment support. This function supports the determination of the optimal treatment by analyzing all applicable treatment methods in consideration of the patient's current condition. Through treatment method analysis, information analysis of factors such as inconsistencies, errors, omissions, and side effects between the treatment methods is possible. This function is the most commonly studied area and provides interaction information between prescribed drugs or checks for side effects when multiple drugs are prescribed together. The third function of CDSSs is medical image analysis. As the performance of deep learning models using image data develops, it is the most commonly used CDSS function related to AI in the medical field.