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디지털헬스케어전공

남주강  기금조교수

Ju Gang Nam

위치분당서울대학교병원 지석영의생명연구소 1110호
연락처031-787-7609
이메일dyuing89@snu.ac.kr
학위박사 / 의학과, 서울대학교 의과대학
분야흉부 영상의학, 흉부 인터벤션
학력
– 2021 서울대학교 의과대학 의학과 박사
– 2018 서울대학교 의과대학 의학과 석사
– 2014 서울대학교 의과대학 의학과 학사
– 2010 서울대학교 자연과학대학 의예과 학사
주요 경력
– 2024 – 현재 분당서울대학교병원 영상의학과 흉부 조교수
– 2020 – 2024 서울대학교병원 영상의학과 흉부 진료전임강사/진료조교수
– 2019 – 2020 서울대학교병원 영상의학과 흉부 전임의
– 2015 – 2019 서울대학교병원 영상의학과 흉부 전공의
– 2014 – 2015 서울대학교병원 인턴
주요 수상 및 영예
– 2025 대한의료인공지능학회(KoSAIM) 교육이사
– 2024 대한의학영상정보학회 (KSIIM) 교육이사
– 2024 대한영상의학회 아큐젠 젊은 연구자상
– 2024 서울대학교병원 명주완 의학상
– 2023 대한영상의학회 학술상 최우수상
– 2023 서울대학교병원 젊은 연구자상
– 2023 서울대학교병원 제중우수논문상
– 2022 보건의료기술 진흥 유공자 보건복지부 장관 표창 (신진연구 부문)
– 2022 대한흉부영상의학회 저술상
최근 연구 성과
– Nam JG, Kang SK, Choi H, Hong W, Park J, Goo JM, Lee JS, Park CM. Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network. European Radiology. 2024;97(1155):632–639
– Lee T, Ahn SY, Kim J, Park JS, Kwon BS, Choi SM, Goo JM, Park CM, Nam JG. Deep learning-based survival prediction in idiopathic pulmonary fibrosis using chest radiographs. European Radiology. 2023
– Jo GD, Ahn C, Hong JH, Kim DS, Park J, Kim H, Kim JH, Goo JM, Nam JG. 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT. BMC Medical Imaging. 2023
– Nam JG, Choi Y, Lee SM, Yoon SH, Goo JM, Kim H. Prognostic value of deep learning–based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis. European Radiology. 2023
– Nam JG, Hwang EJ, Kim J, Park N, Lee EH, Kim HJ, Nam M, Lee JH, Park CM, Goo JM. Artificial intelligence improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology. 2023
– Nam JG, Hong H, Choi SH, Park CM, Goo JM, Kim YT, Kim H. Histopathologic basis for a chest CT deep learning survival prediction model in patients with lung adenocarcinoma. Radiology. 2022
Education
– Ph.D., Department of Medicine, Seoul National University College of Medicine, Seoul, Korea (2021): Medicine
– M.S., Department of Medicine, Seoul National University College of Medicine, Seoul, Korea (2018): Medicine
– B.S., Department of Medicine, Seoul National University College of Medicine, Seoul, Korea (2014): Medicine
– B.S., Department of Pre-Medicine, College of Natural Sciences, Seoul National University, Seoul, Korea (2010): Pre-Medicine
Professional Experience
– Assistant Professor, Department of Radiology (Thoracic Division), Seoul National University Bundang Hospital, Seoul, Korea (2024–present)
– Clinical Assistant/Associate Professor, Department of Radiology (Thoracic Division), Seoul National University Hospital, Seoul, Korea (2020–2024)
– Clinical Fellow, Department of Radiology (Thoracic Division), Seoul National University Hospital, Seoul, Korea (2019–2020)
– Resident, Department of Radiology (Thoracic Division), Seoul National University Hospital, Seoul, Korea (2015–2019)
– Intern, Seoul National University Hospital, Seoul, Korea (2014–2015)
Honors and Awards (Selected)
– 2025 Director of Education, Korean Society of Artificial Intelligence in Medicine (KoSAIM)
– 2024 Director of Education, Korean Society of Imaging Informatics in Medicine (KSIIM)
– 2024 Acuzen Young Investigator Award, Korean Society of Radiology
– 2024 Myung Joo Wan Medical Award, Seoul National University Hospital
– 2023 Grand Prize for Scientific Achievement, Korean Society of Radiology
– 2023 Young Investigator Award, Seoul National University Hospital
– 2023 Jejung Excellent Paper Award, Seoul National University Hospital
– 2022 Minister of Health and Welfare Commendation (Outstanding Young Researcher Category), Ministry of Health and Welfare, Republic of Korea
– 2022 Publication Award, Korean Society of Thoracic Radiology
Recent Research Achievements
– Nam JG, Kang SK, Choi H, Hong W, Park J, Goo JM, Lee JS, Park CM. Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network. European Radiology. 2024;97(1155):632–639
– Lee T, Ahn SY, Kim J, Park JS, Kwon BS, Choi SM, Goo JM, Park CM, Nam JG. Deep learning-based survival prediction in idiopathic pulmonary fibrosis using chest radiographs. European Radiology. 2023
– Jo GD, Ahn C, Hong JH, Kim DS, Park J, Kim H, Kim JH, Goo JM, Nam JG. 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT. BMC Medical Imaging. 2023
– Nam JG, Choi Y, Lee SM, Yoon SH, Goo JM, Kim H. Prognostic value of deep learning–based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis. European Radiology. 2023
– Nam JG, Hwang EJ, Kim J, Park N, Lee EH, Kim HJ, Nam M, Lee JH, Park CM, Goo JM. Artificial intelligence improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology. 2023
– Nam JG, Hong H, Choi SH, Park CM, Goo JM, Kim YT, Kim H. Histopathologic basis for a chest CT deep learning survival prediction model in patients with lung adenocarcinoma. Radiology. 2022