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https://www.riss.kr/link?id=A107879849
Hwang Eui Jin (Department of Radiology, Seoul National University Hospital, Seoul, Korea.Department of Radiology and Institution of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea.) ; Goo Jin Mo (Department of Radiology, Seoul National University Hospital, Seoul, Korea.Department of Radiology and Institution of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea.Cancer Research Institute, Seoul National University, Seo) ; Yoon Soon Ho (Department of Radiology, Seoul National University Hospital, Seoul, Korea.Department of Radiology and Institution of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea.Department of Radiology, UMass Memorial Medical Center, W) ; Beck Kyongmin Sarah (Department of Radiology, Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.) ; Seo Joon Beom (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.) ; Choi Byoung Wook (Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.) ; Chung Myung Jin (Department of Radiology and Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.) ; Park Chang Min (Department of Radiology, Seoul National University Hospital, Seoul, Korea.Department of Radiology and Institution of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea.) ; Jin Kwang Nam (Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea.) ; Lee Sang Min (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.)
2021
English
KCI등재,SCIE,SCOPUS
학술저널
1743-1748(6쪽)
0
0
상세조회0
다운로드참고문헌 (Reference)
1 Tobia K, "When does physician use of AI increase liability?" 62 : 17-21, 2021
2 Qin ZZ, "Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems" 9 : 15000-, 2019
3 Tajmir SH, "Toward augmented radiologists:changes in radiology education in the era of machine learning and artificial intelligence" 25 : 747-750, 2018
4 Healthcare Bigdata Hub, "Statistics on medical practices"
5 Kuo PC, "Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph" 4 : 25-, 2021
6 Price WN, "Potential liability for physicians using artificial intelligence" 322 : 1765-1766, 2019
7 Lee JH, "Performance of a deep learning algorithm compared with radiologic interpretation for lung cancer detection on chest radiographs in a health screening population" 297 : 687-696, 2020
8 Park SH, "Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction" 286 : 800-809, 2018
9 Ministry of Food and Drug Safety, "Medical device information portal"
10 Eui Jin Hwang, "Implementation of a Deep Learning-Based Computer- Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19" 대한영상의학회 21 (21): 1150-1160, 2020
1 Tobia K, "When does physician use of AI increase liability?" 62 : 17-21, 2021
2 Qin ZZ, "Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems" 9 : 15000-, 2019
3 Tajmir SH, "Toward augmented radiologists:changes in radiology education in the era of machine learning and artificial intelligence" 25 : 747-750, 2018
4 Healthcare Bigdata Hub, "Statistics on medical practices"
5 Kuo PC, "Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph" 4 : 25-, 2021
6 Price WN, "Potential liability for physicians using artificial intelligence" 322 : 1765-1766, 2019
7 Lee JH, "Performance of a deep learning algorithm compared with radiologic interpretation for lung cancer detection on chest radiographs in a health screening population" 297 : 687-696, 2020
8 Park SH, "Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction" 286 : 800-809, 2018
9 Ministry of Food and Drug Safety, "Medical device information portal"
10 Eui Jin Hwang, "Implementation of a Deep Learning-Based Computer- Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19" 대한영상의학회 21 (21): 1150-1160, 2020
11 Ministry of Health and Welfare, "Guideline on reimbursement for innovative medical technology"
12 Nam JG, "Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs" 290 : 218-228, 2019
13 Hwang EJ, "Development and validation of a deep learning–based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs" 69 : 739-747, 2019
14 Hwang EJ, "Development and validation of a deep learning–based automated detection algorithm for major thoracic diseases on chest radiographs" 2 : e191095-, 2019
15 Nam JG, "Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs" 57 : 2003061-, 2021
16 김동욱, "Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers" 대한영상의학회 20 (20): 405-410, 2019
17 Park S, "Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings" 30 : 1359-1368, 2020
18 Lee JH, "Deep learning-based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals" 31 : 1069-1080, 2021
19 Sim Y, "Deep convolutional neural network–based software improves radiologist detection of malignant lung nodules on chest radiographs" 294 : 199-209, 2020
20 황의진, "Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges" 대한영상의학회 21 (21): 511-525, 2020
21 Khan FA, "Chest x-ray analysis with deep learningbased software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease" 2 : e573-e581, 2020
22 Tang A, "Canadian association of radiologists white paper on artificial intelligence in radiology" 69 : 120-135, 2018
23 Hwang EJ, "COVID-19 pneumonia on chest X-rays: performance of a deep learning-based computer-aided detection system" 16 : e0252440-, 2021
24 Murphy K, "COVID-19 on chest radiographs: a multireader evaluation of an artificial intelligence system" 296 : E166-E172, 2020
25 Annarumma M, "Automated triaging of adult chest radiographs with deep artificial neural networks" 291 : 196-202, 2019
26 Hwang EJ, "Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration" 30 : 6902-6912, 2020
27 Sung J, "Added value of deep learning-based detection system for multiple major findings on chest radiographs: a randomized crossover study" 299 : 450-459, 2021
28 우현식, "2017년 대한민국 영상의학검사 원격판독의 실태: 수련병원 및 원격판독기관 설문조사와 인터뷰" 대한영상의학회 80 (80): 490-502, 2019
29 최문형, "2017년 대한민국 영상의학검사 원격판독의 실태: 대한영상의학회 회원 설문 조사" 대한영상의학회 80 (80): 684-703, 2019
학술지 이력
연월일 | 이력구분 | 이력상세 | 등재구분 |
---|---|---|---|
2023 | 평가예정 | 해외DB학술지평가 신청대상 (해외등재 학술지 평가) | |
2020-01-01 | 평가 | 등재학술지 유지 (해외등재 학술지 평가) | |
2016-11-15 | 학회명변경 | 영문명 : The Korean Radiological Society -> The Korean Society of Radiology | |
2010-01-01 | 평가 | 등재학술지 유지 (등재유지) | |
2007-01-01 | 평가 | 등재학술지 선정 (등재후보2차) | |
2006-01-01 | 평가 | 등재후보 1차 PASS (등재후보1차) | |
2003-01-01 | 평가 | 등재후보학술지 선정 (신규평가) |
학술지 인용정보
기준연도 | WOS-KCI 통합IF(2년) | KCIF(2년) | KCIF(3년) |
---|---|---|---|
2016 | 1.61 | 0.46 | 1.15 |
KCIF(4년) | KCIF(5년) | 중심성지수(3년) | 즉시성지수 |
0.93 | 0.84 | 0.494 | 0.06 |