## 1 Introduction
### Relevance of Explainability in Medical AI  
Despite the difficulties, it is clear that there are quite a few potential benefits of explainable and transparent models, motivating the renewed interest in the field (Figure 1). Besides helping increase the acceptance of AI models, enabling clinical professionals to take advantage of their conveniences and addressing ethical concerns, explainability could also the allow better validation of these systems and facilitate troubleshooting during development [15]. Going even further, it could also be argued that explainable models could assist knowledge discovery in some cases, if we are able to understand the intrinsic relations hidden in the data found by AI models [16]. When discussing the potential benefits and relevance of medical XAI, we believe it is also crucial to address the often-overlooked aspect of data quality, closely intertwined with ethical trust, algorithmic bias, and interpretability concerns. The scarcity and often poor quality of medical data pose significant technical and methodological challenges for AI in healthcare, that could potentially be easier to identify and mitigate thanks to XAI. While data-driven models offer the advantage of not requiring explicit expert knowledge, they can become fragile and prone to failure due to low-quality or artifact-ridden data [17], leading to issues like overfitting and the hampering of model generalisation. This problem also stems from data biases such as poorly represented populations [18; 19], variations in data collection conditions, and the incorporation of human biases, particularly in the field  
Figure 1: Main objectives and advantages of XAI approaches, categorised into technical, scientific, and ethical dimensions. of medicine, where the individuality and particularities of each patient and clinician will exert great influence [20]. However, XAI techniques hold promise in helping to detect and address these issues before deploying faulty models [21], emphasizing the need for rigorous data curation and comprehensive testing, that should go hand in hand with the development of XAI approaches in healthcare [22; 23; 24]. In addition, explainability might become a future legal requisite in fields such as medicine, since patients need to be properly informed about how their personal data is being used. Currently, some legal regulations, such as the GDPR (General Data Protection Regulation) in the European Union [25], are starting to include the requirement of transparent AI when dealing with sensitive data, pointing out that, in the future, the use of explainability in AI-based systems might be further enforced [26]. For those reasons, much of the recent literature is increasingly becoming available, appraising the benefits of XAI and presenting many different new methods which claim to be appropriate solutions. This is especially required in the field of healthcare, where responsibility and understanding become peculiarly important issues. However, there is persistent controversy in the field, since some researchers remain skeptical about current approaches, stating that capabilities of XAI may be overestimated, while several important limitations may be disregarded [27]. Such a controversy advocates for the rigorous internal and external validation of AI models [28]. This scoping review aims to be a starting point for those who want to get a wider and comprehensive view of the field of XAI applied to medicine. There are already a few review articles on this topic, as shown in our preliminary research, either more general [29; 30] or focused in more specific subfields [31; 32; 33; 34; 35; 36].