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本專案之互動式3D教學平台

學生使用Microsoft HoloLens 觀看由AI分割的3D器官

將3D影像投影到大螢幕與學生電腦上課

使用AI重組等同於過去大體切片的3D模型

Information
Traditional anatomy education relies primarily on 2D CT/MRI slice images, making it difficult for students to build three-dimensional spatial comprehension. Surveys showed that 80% of students found memorizing large volumes of anatomical terminology challenging, and 50% lacked intuitive visual aids. The Department of Digital Medicine, Division of Applied Medicine, collaborated with IntelliGen Technology Inc. to introduce an AI-driven 3D Medical Imaging Anatomy Education Platform (Anatomy Cloud), integrating fully automated organ segmentation with interactive learning design, implemented in the Year 3 medical anatomy curriculum.\n\nCore system features include: (1) Fully automated AI segmentation — deep learning models automatically convert CT images into up to 40 colored 3D organ models, eliminating the need for manual layer-by-layer annotation; (2) Interactive 3D manipulation — students can rotate, zoom, and toggle organ transparency in a browser for intuitive spatial understanding; (3) AI-powered Q&A learning — integrated large language model provides real-time anatomy knowledge responses to support self-directed learning; (4) Cross-device compatibility — supports computers, tablets, and smartphones with no time or location restrictions; (5) Cloud-based collaboration — teachers can customize and instantly share 3D teaching materials with the entire class, reducing lesson preparation time by approximately 40%. The platform combines high accessibility with high interactivity, effectively addressing the limitations of traditional teaching.
Benefits

I. Implementation Experience and Process\n\nThe institution introduced the AI-driven 3D Medical Imaging Anatomy Education Platform (Anatomy Cloud) into the Year 3 medical anatomy curriculum. Pre-implementation surveys (N=20) were conducted to identify student learning challenges; post-implementation assessments (N=14) employed a dual-track approach combining quantitative academic performance measurement and satisfaction questionnaires.\n\nII. Quantitative Learning Outcomes\n\n(1) Significant academic improvement: The final written examination mean score improved from 75.4 to 78.7 (+3.3 points); the pass rate rose markedly from 80.5% to 98.0% (+17.5 percentage points); the proportion of failing students (<60 points) decreased from 19.5% to 2.0%, with the number of students scoring 50–59 dropping from 7 to 0.\n\n(2) Comprehensive enhancement of learning competencies: Post-implementation surveys showed that 100% of students reported improved spatial visualization (92.9% "significantly improved"); 100% reported "significant/substantial improvement" in anatomical structure identification; 100% found the content "very helpful" for understanding clinical applications; overall learning satisfaction reached 4.71/5.0; recommendation intention 4.57; continued-use intention 4.64; and 92.9% of students reported enhanced learning motivation (mean 4.43).\n\n(3) Reduced teacher workload: Lesson preparation time was reduced by approximately 40%, enabling teachers to focus on higher-order instructional interaction.\n\nIII. Extended Applications and Dissemination\n\nThe platform has been extended to pre-operative image communication in Surgery, Radiology, and other clinical departments, with over 50 successful surgical cases completed in collaboration with multiple Taiwanese medical institutions. Future plans include expanding the platform to nursing and physical therapy training, establishing cross-institutional anatomy education resource-sharing mechanisms, and realizing sustainable development of smart medical education.

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