Professor Kyung Oh Jung conducts research to visualize biological processes at the molecular level, aiming for clinical applications by progressing from cellular to animal models. Recently, it has shown even greater potential for advancement by leveraging artificial intelligence (AI) in his research.
He has received significant attention for successfully tracking a single breast cancer cell in vivo for the first time in the world, using molecular imaging and image processing technology.
In the AI Driven Precision Medicine Lab, he conducts research on tumor diagnosis and treatment at the cellular level, as well as animal experiments utilizing various imaging equipment such as fluorescence, bioluminescence, PET, and MRI. Furthermore, actively engaging in extensive collaborative research converging with AI.
We are subdivided into 4 research teams based on subject area,
(TME, BioEngineering, Skin, Natural Extract/Obesity/Aging)
conducting close joint research with 15 collaborating institutions.
TME : TME exosome, metastasis exosome genetic analysis
BioEngineering : immune cell single-cell tracking FDG experiment, radioisotopes labelling
Skin : hair regeneration, cosmetics, Gardenia Jasminoides-enhanced exosomes
Natural Extract : Ginseng-enhanced exosomes
TME
Member : Seong Je Hong*, Se Yeon Jang
Collaboration
- Therabest
- RayMed
- Stanford School of Medicine
- 서울대학교병원
- 성균관대학교
- 가톨릭대학교 의과대학
Immunology/
BioEngineering
Member : Ju Hyung Jeon*, Hyun Ji Lee, Yeon Jin Lim
Collaboration
- 한국원자력의학원
- 경희대학교 의과대학
- Stanford School of Medicine
- Therabest
- 전북대학교
Skin
Member : Hyun Chul Jo*, Seong Tae Pi
Collaboration
- KAIST
- Stanford School of Medicine
- Dr.J Lab
- 중앙대학교병원
- 제주대학교
Natural Extract/
Obesity/Aging
Member : Hyun Ah Kim*, Yeon Kyo Lim
Collaboration
- 중앙대학교 의과대학
- Stanford School of Medicine
- 제주대학교
- 경희대학교 한의과대학
We are broadening the utility of Ai in cutting-edge research areas.
Vision AI
Radiation Anti-Cancer Software
Single-Cell Tracking
Biomarker Discovery
microRNA research
Material Science
Alphafold
Dosage form
Clinical/Public
Data Analysis
Hair loss patient