# XAI-Based Clinical Decision Support Systems: A Systematic Review
## 2 Related Work
### XAI-Based CDSS  
Explainable AI (XAI) [5], has emerged to overcome the black-box [6] problem of deep learning models, which means that deep learning models have the highest performance compared to other rule-based or machine learning models but have the limitation of lacking interpretability. This can be described as the \"performance-interpretability trade-off\" and is shown in Figure 3[65; 66]. Performance is highest for deep learning, followed by machine learning models (decision tree, nearest neighbors, and Bayesian network), and rule-based models; however, transparency (interpretability) is inversely proportional. In other words, applying XAI to deep learning models is useful for explaining the reason and logic behind the results predicted by the model to ensure transparency and reliability of the results with high-performance deep learning. With attempts to apply XAI in various fields [67], XAI has gained attention as a solution to the uncertainty problem in CDSSs where accuracy and reliability are important [66]. The description techniques used in XAI can be broadly categorized into scoop-, model-, complexity-, and methodology-based techniques [67]. The most popular XAI methods in recent research include Shapley additive explanations (SHAP) [68], local interpretable model-agnostic explanations (LIME) [69], post hoc interpretability [70], and gradient-weighted class activation mapping (GradCAM) [70]. Scoop-based techniques determine the contribution of data based on the importance features to train the AI model. A prominent example is the local explainers method (LIME) [71], which is a method specific to a particular instance or outcome. LIME is a technique for generating local approximations to model predictions. For example, given the task of predicting emotions with a deep neural network, LIME focuses on surrounding local words to highlight words that are important for a particular prediction. LIME directly \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Features**} & \multicolumn{4}{c}{**Functions**} \\ \cline{3-5}  & & & **Diagnosis and Treatment** & **Alert and Monitoring** & **Applied Domain** \\ \hline \multirow{5}{*}{CNN} & \multirow{5}{*}{Features extracted were analyzed by connecting them to convolutional layers} & [42] & ✓ & Sleep Apnea \\ \cline{3-5}  & & [43] & ✓ & Sleep Apnea \\ \cline{3-5}  & & [45] & ✓ & Melanoma \\ \cline{3-5}  & & [44] & & ✓ & Ambulance assignment \\ \hline \multirow{2}{*}{RNN} & Process time series data to find sequential patterns in the data & [47] & ✓ & Heart Failure \\ \cline{3-5}  & & [52] & ✓ & Sleep Apnea \\ \hline \multirow{5}{*}{LSTM} & \multirow{5}{*}{Useful when dealing with long-term data and utilizing historical data to predict what to expect in the future.} & [54] & ✓ & Sleep Apnea \\ \cline{3-5}  & & [56] & ✓ & Lung nodules \\ \cline{3-5}  & & [57] & ✓ & Lung cancer \\ \hline \multirow{5}{*}{Reinforcement Learning} & \multirow{5}{*}{Trains software to make decisions to achieve the most optimal results} & [58] & ✓ & Pulmonary tuberculosis \\ \cline{3-5}  & & [60] & ✓ & Medications \\ \cline{3-5}  & & [3] & ✓ & Protecting patient information \\ \hline \multirow{3}{*}{Transformer} & Specializes in processing text data with large language models using transformers in an encoder–decoder structure} & [61] & ✓ & Radiology \\ \cline{3-5}  & & [64] & ✓ & Diagnosis code categorization \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Non-knowledge-based CDSSs. explain how the model's input data change the outcome; after training the model, it can make guesses about samples that have not appeared before [72]. For COVID-19, LIME and traditional machine learning models were combined to identify the features that had the greatest impact on medical history, time of onset, and patients' primary symptoms [68]. Similarly, an LSTM model was used in a study on depressive symptom detection, in conjunction with a LIME approach to identify text suggestive of depressive symptoms [73].