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Explainable Artificial Intelligence (XAI) is an emerging research topic ofmachine learning aimed at unboxing how AI systems' black-box choices are made.This research field inspects the measures and models involved indecision-making and seeks solutions to explain them explicitly. Many of themachine learning algorithms can not manifest how and why a decision has beencast. This is particularly true of the most popular deep neural networkapproaches currently in use. Consequently, our confidence in AI systems can behindered by the lack of explainability in these black-box models. The XAIbecomes more and more crucial for deep learning powered applications,especially for medical and healthcare studies, although in general these deepneural networks can return an arresting dividend in performance. Theinsufficient explainability and transparency in most existing AI systems can beone of the major reasons that successful implementation and integration of AItools into routine clinical practice are uncommon. In this study, we firstsurveyed the current progress of XAI and in particular its advances inhealthcare applications. We then introduced our solutions for XAI leveragingmulti-modal and multi-centre data fusion, and subsequently validated in twoshowcases following real clinical scenarios. Comprehensive quantitative andqualitative analyses can prove the efficacy of our proposed XAI solutions, fromwhich we can envisage successful applications in a broader range of clinicalquestions.

Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond