## 4 Results
### RQ3: What Benefits Have Beent Reported When Addressing Different Aspects of the Use of XAI in CDSS? Several benefits of XAI used in CDSS have been reported. Some researchers presented their XAI-based approaches to doctors or clinicians and collected feedback for usability and acceptability validation. [PERSON] [106] created vignettes of intelligent systems including a hypothetical CDSS and asked participants (graduate human-computer interaction students) to write down any questions that they would want to ask the system to help them determine whether or not to accept or reject the system recommendation. They reported that XAI could provide different information types to make intelligent systems explainable and more acceptable and trustworthy to users. [PERSON] et al. [111] developed an XAI question bank \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Paper** & **XAI Method** & **Model-Agnostic/Specific** & _Ante-Hoc/Post-Hoc_ & **Local/Global** \\ \hline [PERSON] et al. [118] & SHAP [79] for attribution, LORE [129] for counterfactual rules, MOEA/D [130] for sensitivity analysis & & & \\ \hline [PERSON] et al. [119] & Pre-processing to obtain regions, application of randomised Deep Neural Networks on each region and extraction of regional abnormality representations in the form of a map & & & \\ \hline [PERSON] et al. [120] & SHAP [79] for summary plot and partial dependence plot & & & \\ \hline [PERSON] et al. [122] & BiGRU with attention mechanism to show the contribution of each fragment of text to the prediction & & & \\ \hline [PERSON] et al. [123] & Visualised the created preference model using rainbow boxes [131] & agnostic & & & \\ \hline [PERSON] et al. [124] & CLFNN, which autonomously generates fuzzy rules to provide human-like reasoning & & & \\ \hline [PERSON] et al. [125] & Semantically interpretable FRBS with the integration of semantic ontology-based reasoning & & & \\ \hline [PERSON] et al. [126] & Visual (using rainbow-boxes [131] and a polar multidimensional scaling scatter plot) case-based reasoning approach & & & \\ \hline [PERSON] et al. [128] & RFGB, a statistical relational learning method which uses tree models and provides explanations in terms of features of interest & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CDSS classified by XAI method. to bridge the spaces of user needs for AI explainability and technical capabilities provided by XAI work. They interviewed 20 participants to identify gaps between the current XAI algorithmic work and practices to create explainable AI products. The results showed that XAI could gain further insights or evidence, and thus enhance decision confidence or generate the hypothesis about causality. In some cases, users also believed that the interpretation of AI decisions might alleviate their own decision-making biases. XAI can also adapt usage or interaction behaviors to utilize the AI better [111]. [PERSON] et al. [116] developed CheXplain that enables physicians to explore and understand AI-enabled chest X-ray analysis. They asked 39 referring physicians and 38 radiologists to summarize how CheXplain changed their understanding of the underlying AI and how such systems can be integrated into their existing workflow. They showed that XAI provides implications for how physicians can explore and understand data-driven, AI-enabled medical imaging analysis to assist physicians in the medical decision-making process. [PERSON] et al. [127] introduced the critical type of information needs of medical experts to an AI Assistant. They interviewed 21 pathologists to learn about the type of information they desired from the AI assistant. Their findings revealed that users seeking a second opinion compare their information needs to the collaborative mental models they have developed and their compatibility with their diagnostic models.