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