## 5 Discussion  
The massive amounts of data generated and increasing availability of computational resources in healthcare systems make many clinical problems ripe for the development of AI applications. These systems will make diagnosis, treatment, prognostic efforts, follow-up, and decision-making more straightforward, precise, and efficient. This is aided by the fact that physicians worldwide are becoming more receptive towards, and accepting of, AI solutions [12]. However, medical experts struggle with the gap between what is output by an ML-based solution and human explanations. To close the gap requires interdisciplinarywork that studies how humans explain, formalizes the patterns in algorithmic forms, and explains outputs in a transparent, easy to interpret manner [67]. A rapid advancement of XAI is evident, and the recent study by [PERSON] et al. [132] has identified four main areas of focus: methods for explaining complex black-box models, methods for creating white-box models, methods that promote fairness and restrict the existence of discrimination, and methods for analyzing sensitivity of model predictions. The authors noticed a significant amount of work on explaining complex black-box models, especially on neural networks [132], probably due to the fact that there is great potential in terms of complex analyses and performance. On the other hand, white-box models are described as more challenging to create and, as a result, they seem to have lost their popularity among developers, while despite the progress in methods to promote fairness, the studies that have addressed this issue are also limited. However, there is an open debate on whether or not XAI in these contexts is necessary and/or worth the substantial overall cost [19]. Nevertheless, XAI may lead to greater uptake and use of CDSS, and may become a requirement in the future due to societal, regulatory, and ethical pressures [18], which could make the difference between success and failure of the system [19]. In some scenarios, explainability of AI output will be a requirement for the output to be used at all, in particular in high-stakes or high-pressure scenarios [3]. Currently, most of the decisions made by AI-based CDSS cannot be interpreted in a transparent way potentially limiting the uptake, trust, and usability of these systems in practice [20; 133]. On the other hand, there are some studies supporting that XAI is not always necessary [46; 134; 135]. [PERSON] [134] defends the ability to produce results and empirically verify their accuracy as more important than the ability to explain how such results are produced. [PERSON] [135] explains this argument using examples of the lack of explainability of many processes in our daily lives, for example, how cars, computers, cell phones, or even our brains work. [PERSON] [46] argues that the short-term goal of building trust with doctors by developing transparent models might clash with the longer-term goal of improving health care. Note that [PERSON] [136] states that the opaqueness of models such as deep neural networks is not what is limiting our understanding, but rather the \"link uncertainty\", meaning the empirical link between the model's features and the [PERSON] studied. Additionally, [PERSON] et al. [137] shed light on the difficulties that are presented when rendering ML models explainable for healthcare purposes, highlighting that the implementation of such systems requires overlap between different disciplines and professions. The \"right\" level of explainability required depends on many factors and is context and resource specific [138]. However, explanations should be at least potentially actionable, parsimonious, and timely [69], warranting further research [19]. Ultimately, it is presumed that explainable CDSS will build trust with clinicians leading to increased adoption of ML-based systems in clinical practice [12; 21; 69; 139].