Research
Clinical data-driven AI, quantitative imaging, and evidence synthesis in thoracic radiology.
Research themes
Chest X-ray foundation models
How patient characteristics should be encoded in chest X-ray foundation models for accuracy and fairness.
Quantitative CT & automated measurement
Automated measurement and quantification of cardiovascular and airway findings on routine chest CT.
- Ongoing projects
Medical vision-language models
3D CT/MRI vision-language and prediction models, in collaboration with SNU IMSI Lab (advisor).
- CVPR 2026 · Medic-AD
- MICCAI ELAMI 2025 · 3D CT VLM
- ISBI 2026 · PNI prediction on 3D MRI
LLMs in radiology education
Validating LLM-generated learning materials and multi-LLM pipelines for radiology education.
Systematic review & meta-analysis
Meta-analyses of interventional and diagnostic imaging procedures such as CBCT-guided lung biopsy.
Population imaging cohorts
Large screening cohorts linking incidental chest CT findings to long-term outcomes.
Collaboration
IMSI Lab, Seoul National University
Imaging-Driven Medical Superintelligence Group (PI: Prof. Namjoon Kim). Clinical consultation for imaging-driven AI research.
Work with me
For collaborations on thoracic imaging data, imaging AI, or meta-analysis, please use the contact form or email. honeia11@gmail.com