## 4 Results
### RQ3: What Benefits Have Beent Reported When Addressing Different Aspects of the Use of XAI in CDSS?
This suggests that AI transparency in collaborative decision making could allow experts to integrate AI assistants into daily practice and gain a richer understanding of the key issues they find. [PERSON] et al. [126] presented a visual and interpretable case-based reasoning system. The system displays the dimension names and their associated values to explain why similar cases are similar to the query case and on which dimensions and values the similarities are contained. Such a visual interface can explain the reasoning process to the user, the user can consider their own personal knowledge to enrich the reasoning process, and automatic algorithms can better formalize the visual reasoning process. [PERSON] et al. [126] reporting that a visual approach could explain why cases are similar via the visualization of shared patient characteristics. This was useful to medical experts, as the physician needs to be aware of the recommendations and confident in their application and use. They presented their interface to 11 medical experts for usability and acceptability validation, demonstrating that XAI could provide the user with a good indication of the confidence level of their choice [126]. Even though some other XAI-based approaches have not yet been tested on users, the benefits of the XAI presented in these works still seem likely to be useful in practice. [PERSON] et al. [128] indicated that XAI could support specific rational reasoning processes, enabling CDSS to support their decisions with understandable interpretations to users with/without ML expertise. [PERSON] et al. [118] identified that XAI could support different explanation types by articulating how people understand events or observations through explanations and can be leveraged to mitigate decision biases and cognitive biases [98; 103; 112]. Moreover, XAI facilities do support specific rational reasoning processes and can be designed to target decision errors. They could help organize explanations, identify gaps to develop new explanations given an unmet reasoning need, and identify appropriate mitigation strategies to select specific XAI facilities [118]. As discussed in Section 2.2.1, in 2016 the European Union passed the GDPR which has been interpreted as a requirement for any decision made based on an algorithm to be explainable to the user [106]. XAI could help the user understand when to trust a model and why an error may occur [97; 116]. Therefore, XAI can support compliance with the GDPR [98; 103; 106]. Moreover, [PERSON] et al. [120] supported that XAI could provide a description of the cumulative importance of domain-specific features, and a visual explanation of their importance would enable the physicians to understand the critical features in the model intuitively. Therefore, explainability of the support system can improve the acceptability of CDSS by clinicians [48] increase the chances of the complex AI systems' adoption and clinical feasibility of a novel CDSS [105; 121]. XAI therefore could greatly enhance the effectiveness of decision-support and clinician confidence [128] especially when high stakes decisions are being made [127], which is the key factor for the success of the model in the practical use stage [122; 124].