Professor Lee is an expert in the fields of data mining, social determinant and technology management. His achievement in research, teaching and thesis supervision includes: (1) designing 12 research projects and publishing 32 international journal articles (including articles in European Radiology, Journal of Dental Research and Nature’s Sister Journal Scientific Reports); (2) teaching 3 classes with full responsibility (Economic Evaluation of Medical Technology, Medical Sociology, Health Science and Artificial Intelligence) at world-class medical schools (Korea/Yonsei); (3) supervising 1 doctoral and 12 master’s dissertations as the committee member; and (4) teaching a class for faculty members (Artificial Intelligence in Medicine). He combines applied mathematics and social science to develop artificial intelligence decision support systems for disease diagnosis, prognosis, prevention and management. For example, his article published in Scientific Reports (SCI IF 4.011) investigates the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer. Likewise, his article published in Journal of Dental Research (SCI IF 5.125), develops a diagnostic tool to automatically detect osteoarthritis of the temporomandibular joint (TMJOA) from cone beam computed tomography (CBCT) images using artificial intelligence. CBCT images of patients diagnosed with temporomandibular disorder (TMD) were included for image preparation. SSD, an object detection model, was trained using 3,514 sagittal CBCT images of the temporomandibular joint (TMJ) that showed signs of osseous changes in the mandibular condyle. The region of interest (condylar head) was defined and classified into two classes: indeterminate for TMJOA and TMJOA, according to the Image Analysis Criteria of Research Diagnostic Criteria for TMD diagnosis. The model was tested using two sets of 300 images total. The average accuracy, precision, recall, and F1 score over the two test sets were 0.86, 0.85, 0.84, and 0.84, respectively. Automated detection of TMJOA from sagittal CBCT images is possible by using a deep neural networks model. It may be used to support clinicians with diagnoses and decision making for treatments of TMJOA. In a similar context, his article published in European Radiology (SCI IF 5.300) investigates machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and angiogenesis properties on magnetic resonance imaging (MRI). This prospective study examined 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived. Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes (p < 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes (p < 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space (Ve). Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer.