Objective: Traditional medical image visualization technologies present several limitations including the poor intuitiveness of 2D images, overlapping imaging blind spots, and insufficient interactivity. These shortcomings make them unable to satisfy the demands of precision medicine and medical education. This study aims to develop a universal medical image demonstration system based on extended reality (XR) technology, which is compatible with devices such as the HoloLens. By employing 3D visualization and multimodal interaction design, the system provides a more efficient and intuitive approach for clinical diagnosis, medical education, and surgical simulation. Methods: The system employs Unreal Engine as the core platform for architecture framework construction, supporting customized medical image visualization for XR devices. Real chest CT images were utilized in this study. Image segmentation was performed using 3D Slicer, while mask preprocessing was conducted via Anaconda. The masked images and raw data were then imported into Unreal Engine. The core framework of the proposed system was developed using Unreal Engine’s Blueprint visual scripting and Unreal Motion Graphics (UMG) interface designer. A self-directed learning assessment experiment was designed to evaluate the efficacy and performance of the system. Results: The experimental group achieved a pulmonary window recognition accuracy of 82%, a mediastinal window accuracy of 88%, and an overall accuracy of 85%. By comparison, the control group exhibited a pulmonary window accuracy of 50%, a mediastinal window accuracy of 44%, and an overall accuracy of 47%. Conclusions: The medically oriented imaging demonstration system significantly improves recognition accuracy and learning effectiveness, verifying its practical utility and validity.