Research

Clinical data-driven AI, quantitative imaging, and evidence synthesis in thoracic radiology.

Chest X-ray AI

How patient characteristics should be encoded in chest X-ray foundation models for accuracy and fairness.

Quantitative CT

Automated measurement and quantification of cardiovascular and airway findings on routine chest CT.

Vision-Language

3D CT/MRI vision-language and prediction models, in collaboration with SNU IMSI Lab (advisor).

LLM × Education

Validating LLM-generated learning materials and multi-LLM pipelines for radiology education.

Evidence Synthesis

Meta-analyses of interventional and diagnostic imaging procedures such as CBCT-guided lung biopsy.

Population Imaging

Large screening cohorts linking incidental chest CT findings to long-term outcomes.

Collaboration

Imaging-Driven Medical Superintelligence Group (PI: Prof. Namjoon Kim). Clinical consultation for imaging-driven AI research.

Open to collaborate

For collaborations on thoracic imaging data, imaging AI, or meta-analysis, please reach out by email. [email protected]