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      KCI등재 SCI SCIE SCOPUS

      Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do

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      https://www.riss.kr/link?id=A106053884

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      다국어 초록 (Multilingual Abstract)

      Artificial intelligence (AI) is projected to substantially influence clinical practice in the foreseeable future. However, despite the excitement around the technologies, it is yet rare to see examples of robust clinical validation of the technologies...

      Artificial intelligence (AI) is projected to substantially influence clinical practice in the foreseeable future. However, despite the excitement around the technologies, it is yet rare to see examples of robust clinical validation of the technologies and, as a result, very few are currently in clinical use. A thorough, systematic validation of AI technologies using adequately designed clinical research studies before their integration into clinical practice is critical to ensure patient benefit and safety while avoiding any inadvertent harms. We would like to suggest several specific points regarding the role that peer-reviewed medical journals can play, in terms of study design, registration, and reporting, to help achieve proper and meaningful clinical validation of AI technologies designed to make medical diagnosis and prediction, focusing on the evaluation of diagnostic accuracy efficacy. Peer-reviewed medical journals can encourage investigators who wish to validate the performance of AI systems for medical diagnosis and prediction to pay closer attention to the factors listed in this article by emphasizing their importance. Thereby, peer-reviewed medical journals can ultimately facilitate translating the technological innovations into real-world practice while securing patient safety and benefit.

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      참고문헌 (Reference)

      1 Interview with Dr, "Ziad Obermeyer on how collaboration between doctors and computers will help improve medical care--Supplement" 377 : 1209-1211, 2017

      2 Verghese A, "What this computer needs is a physician: humanism and artificial intelligence" 319 (319): 19-20, 2018

      3 "Video from RSNA 2017: how will AI change radiology?"

      4 Collins GS, "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement" 350 : g7594-, 2015

      5 Clarke R, "The properties of high-dimensional data spaces: implications for exploring gene and protein expression data" 8 (8): 37-49, 2008

      6 Fryback DG, "The efficacy of diagnostic imaging" 11 (11): 88-94, 1991

      7 "The curse of dimensionality in classification"

      8 Hastie TJ, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed." Springer 2009

      9 Bossuyt PM, "STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies" 277 (277): 826-832, 2015

      10 Obermeyer Z, "Predicting the future - big data, machine learning, and clinical medicine" 375 (375): 1216-1219, 2016

      1 Interview with Dr, "Ziad Obermeyer on how collaboration between doctors and computers will help improve medical care--Supplement" 377 : 1209-1211, 2017

      2 Verghese A, "What this computer needs is a physician: humanism and artificial intelligence" 319 (319): 19-20, 2018

      3 "Video from RSNA 2017: how will AI change radiology?"

      4 Collins GS, "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement" 350 : g7594-, 2015

      5 Clarke R, "The properties of high-dimensional data spaces: implications for exploring gene and protein expression data" 8 (8): 37-49, 2008

      6 Fryback DG, "The efficacy of diagnostic imaging" 11 (11): 88-94, 1991

      7 "The curse of dimensionality in classification"

      8 Hastie TJ, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed." Springer 2009

      9 Bossuyt PM, "STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies" 277 (277): 826-832, 2015

      10 Obermeyer Z, "Predicting the future - big data, machine learning, and clinical medicine" 375 (375): 1216-1219, 2016

      11 Larson DB, "Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs" 287 (287): 313-322, 2018

      12 Park SH, "Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction" 286 (286): 800-809, 2018

      13 Yasaka K, "Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR images" 287 (287): 146-155, 2018

      14 Luo W, "Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view" 18 (18): e323-, 2016

      15 Kahn CE Jr, "From images to actions: opportunities for artificial intelligence in radiology" 285 (285): 719-720, 2017

      16 Korevaar DA, "Facilitating prospective registration of diagnostic accuracy studies: a STARD initiative" 63 (63): 1331-1341, 2017

      17 Ehteshami Bejnordi B, "Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer" 318 (318): 2199-2210, 2017

      18 Ting DS, "Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes" 318 (318): 2211-2223, 2017

      19 Gulshan V, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs" 316 (316): 2402-2410, 2016

      20 Chartrand G, "Deep learning: a primer for radiologists" 37 (37): 2113-2131, 2017

      21 Yasaka K, "Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study" 286 (286): 887-896, 2018

      22 Lakhani P, "Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks" 284 (284): 574-582, 2017

      23 이준구, "Deep Learning in Medical Imaging: General Overview" 대한영상의학회 18 (18): 570-584, 2017

      24 INFANT Collaborative Group, "Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial" 389 (389): 1719-1729, 2017

      25 The Lancet, "Artificial intelligence in health care: within touching distance" 390 (390): 2739-, 2018

      26 Thrall JH, "Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success" 15 (15): 504-508, 2018

      27 "An intuitive explanation of convolutional neural networks"

      28 Chen PJ, "Accurate classification of diminutive colorectal polyps using computer-aided analysis" 154 (154): 568-575, 2018

      29 "AI diagnostics need attention" 555 (555): 285-286, 2018

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 SCI 등재 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.48 0.37 1.06
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.85 0.75 0.691 0.11
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