Reading chest images,
and working through the questions they raise.
I read chest CT and radiographs at a hospital, and slowly work through questions from the reading room with AI and quantitative imaging. If our interests overlap, feel free to get in touch.
Research
Details →Chest X-ray foundation models
How patient characteristics should be encoded in chest X-ray foundation models for accuracy and fairness.
Quantitative CTQuantitative CT & automated measurement
Automated measurement and quantification of cardiovascular and airway findings on routine chest CT.
Vision-LanguageMedical vision-language models
3D CT/MRI vision-language and prediction models, in collaboration with SNU IMSI Lab (advisor).
LLM × EducationLLMs in radiology education
Validating LLM-generated learning materials and multi-LLM pipelines for radiology education.
Evidence SynthesisSystematic review & meta-analysis
Meta-analyses of interventional and diagnostic imaging procedures such as CBCT-guided lung biopsy.
Population ImagingPopulation imaging cohorts
Large screening cohorts linking incidental chest CT findings to long-term outcomes.
- Oral presentation at RSNA 2026 (Chicago, Dec 3, session R3-SSCH07)
- Reviewer for MICCAI 2026 · Co-author of Medic-AD at CVPR 2026
Chest Radar
A weekly digest of chest imaging AI (in Korean): key results, what it means in the reading room, limitations, and research seeds.