## 8 Conclusions  
AI has the potential to transform and improve healthcare; nevertheless, without explainable and trustworthy systems, its application will continue to be limited. In this paper, we have reviewed the precedents and background with the state of the art of XAI as applied in medicine. Figure 6 shows the interdisciplinary of the work and the main concepts explored in this review. However, there are some limitations of this review that are worth noting. The field of XAI, much like AI in general, is subject to constant change and evolution. Our study is limited in scope, extending only up to early 2023. Given the rapid pace of developments in these fields, some of the latest advances, especially in ML techniques, might not be comprehensively reflected in this review. Furthermore, it is important to understand the nature of this review. As a scoping review, rather than a meta-analysis, its purpose is to provide an overview of the current state of the field, but it is not exhaustive in coverage. Instead, we aimed to present a critical and conceptual perspective, focusing on key aspects and highlighting the important directions and challenges for future research. Still, several popular approaches and techniques have been discussed providing an overview of the available options in including explainability as an aspect of clinical AI systems (RQ1). The benefits of explainable systems could be considerable: promoting trust, enabling better interpretation of the data and predictions or enhancing the detection of errors, for example. However, the considerable challenges identified in this review need to be overcome in the field of medical XAI. Besides the high dimensionality and black-box nature of many AI models, in medicine, the problem of data quality is especially serious, if we are to be able to develop accurate XAI techniques without the risk of being influenced by unrepresentative or poorly selected or curated and filtered data. Moreover, cognitive limitations to knowledge acquisition regarding clinicians' reasoning are also related to the extent of how far XAI methods might be able to prove useful (RQ2). In order to develop and consolidate further robust explainability methods and interpretable models, making them a useful tool for clinicians to trust medical AI applications and, therefore, supporting their implementation in the real world, future research on this topic should be focused on overcoming these challenges, as well as better analysing user needs, enhancing human-system interaction and studying alternatives such as data fusion or clinicians' feedback to include medical validation and knowledge in different ways without the need to explicitly model it. Moreover, involving medical experts in the process of designing and developing these systems would also help in building more robust models and improving user understanding (RQ3 and RQ4). All in all, the prospects for XAI methods in clinical applications is that they are essential in many ways, but that further research is needed to overcome the current limitations enhancing these techniques in order to build secure, trustworthy, and efficient systems that benefit both patients and clinicians. Conceptualisation, E.G.-C., V.M. and C.A.K. ; methodology, R.G.-A., E.G.-C., R.G.-A. and V.M. ; formal analysis, R.G.-A. and E.G.-C.; investigation, V.M., C.A.K., E.G.-C. and R.G.-A. ; writing--original draft preparation, R.G.-A. ; writing--review and editing, C.A.K., V.M. and E.G.-C.; supervision, V.M. and E.G.-C.; funding acquisition, V.M. and E.G.-C. All authors have read and agreed to the published version of the manuscript. Figure 6: Summary of key concepts explored in this review. **Funding:** This work has been supported by the IAX 'Inteligencia Artificial eXplicable' grant from the Comunidad de Madrid through the call Universidad Politencia de Madrid-Jovenes Investigadores 2022/2024, and the Proyecto colaborativo de integracion de datos genomicos (CICLOGEN) (No.