Papers
1
Total Citations
4
H-Index
1
About
Xiaoyun Ji’s research lies at the intersection of artificial intelligence, medical imaging, and traditional Chinese medicine, with a focus on developing intelligent systems for acupuncture and virtual human modeling. Her most cited work, “Location of acupuncture points based on graph convolution and 3D deep learning in virtual humans” (2023, 4 citations), introduces a novel approach that combines graph convolutional networks with 3D deep learning to automatically locate acupoints such as Dazhu, Fengmen, and Xinshu—key points for treating conditions like back pain and respiratory issues. This method addresses the limitations of conventional image-based or manual techniques, offering a scalable, accurate solution for intelligent acupuncture robots. Ji’s contributions are particularly significant for integrating computational anatomy with clinical practice, bridging the gap between ancient medical knowledge and modern AI. Her work has been recognized for its potential to enhance precision in acupuncture therapy, reduce human error, and enable remote or automated treatments. By leveraging deep learning on virtual human models, she has opened new pathways for personalized medicine and rehabilitation robotics, making her a notable figure in the emerging field of AI-driven traditional medicine.
Research Focus
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Top Papers
- 1