Recent advancements in artificial intelligence (AI), particularly in deep learning, have significantly enhanced AI capabilities but have also led to more complex and less interpretable algorithms. This research addresses the challenge of Explainable AI (XAI) by focusing on enhancing the interpretability of AI decisions through the use of Explainable User Interfaces (XUI). The study identifies two primary knowledge gaps: the predominance of XAI research targeting technically skilled users, neglecting the end-user who often lacks technical expertise, and the insufficient exploration of user-centric design principles in real-world XUI applications. The research adopts the Design Science Research Method (DSRM) to develop an XUI tailored for the FOKUS project, which uses Electrocardiogram (ECG) data to detect myocardial infarctions. The study emphasises the strategic application of interactive design principles such as complementary naturalness, flexibility in explanation methods, and responsiveness through progressive disclosure to improve the system’s interpretability. Notably, sensitivity to context and mind, though not initially implemented, emerged as a critical design principle from the analysis and was subsequently positioned at the pinnacle of a restructured pyramid model of design principles. Key findings highlight the effectiveness of the selected design principles in enhancing interpretability and underscore the importance of involving stakeholders early in the development process to align the XAI and XUI with end-user needs. The research proposes a structured design approach framework for XUI, involving sequential phases from pre-XAI to XUI design, to systematically integrate user feedback and improve the design iteratively. The proposed framework restructured pyramid model of the design principles aim to guide future developments in XAI and XUI, enhancing their practical application and effectiveness in various contexts.