연구진 소개

Professor Keon Wook Kang conducts research on early diagnosis, targeted therapy, and theranostic technology for cancer and other intractable diseases, based on nuclear medicine, molecular imaging, and nanomedicine. His work utilizes various imaging modalities, including fluorescence imaging, bioluminescence imaging, SPECT, PET, and MRI, to non-invasively visualize molecular and cellular events in vivo and to evaluate the biodistribution and therapeutic efficacy of novel drugs. His major research interests include the development of tumor-targeted molecular imaging agents and therapeutics, albumin and biodegradable nanoparticle-based drug delivery systems and theranostic platforms using radiopharmaceuticals. Through translational research that bridges basic life sciences and clinical nuclear medicine, he contributes to the advancement of personalized precision medicine and innovative nuclear medicine therapeutic technologies.

Medical physics is an interdisciplinary field that applies the principles and techniques of physics to medicine for the diagnosis, treatment, and prevention of disease. it plays a critical role in medical imaging, radiation therapy, and nuclear medicine by improving image quality, evaluating and optimizing radiation dose, and enhancing treatment accuracy. The field aims to improve patient safety while increasing the precision and efficiency of healthcare. Furthermore, by integrating emerging technologies such as artificial intelligence and data science, medical physics contributes to the development of next generation medical technologies.

Based on a background in anesthesiology, work has centered on a surgical intensive care unit, focusing on the problems faced by critically ill postoperative patients. Particular attention has been given to how microcirculatory dysfunction develops in septic patients and how it progresses to organ failure, drawing on analysis of clinical data. Research has also turned toward using artificial intelligence to predict the clinical course of critically ill patients. A deep learning model was developed to analyze serial chest radiographs from mechanically ventilated patients and predict changes in oxygenation, and the same data is now being reanalyzed using radiomics to build an interpretable alternative model. Continued effort is directed toward finding ways to apply image based artificial intelligence to the actual care of critically ill patients.

Developing artificial intelligence-enabled multi-modal frameworks that integrate spatial transcriptomics, single-cell transcriptomics, immune phenotyping, and clinical data to uncover mechanisms of cardiovascular aging, identify therapeutic targets, and predict outcomes in aortic aneurysm and calcific aortic valve disease.