How will "democratization of artificial intelligence" change the future of radiologists?

被引：25
作者：
Kobayashi, Yasuyuki [1 ]
Ishibashi, Maki [1 ]
Kobayashi, Hitomi [2 ]
机构：
[1] St Marianna Univ, Dept Med Informat & Commun Technol Res, Grad Sch Med, Sch Med,Miyamae Ku, 2-16-1 Sugao, Kawasaki, Kanagawa 2168511, Japan
[2] Nihon Univ, Sch Med, Div Hematol & Rheumatol, Dept Med, 30-1 Oyaguchi Itabashi Ward, Tokyo 1738610, Japan
来源：
JAPANESE JOURNAL OF RADIOLOGY | 2019年 / 37卷 / 01期
关键词：
Democratization; Artificial Intelligence; Medicine; Radiology; Radiologist; CONVOLUTIONAL NEURAL-NETWORK; DEEP; CLASSIFICATION; CT;
D O I：
10.1007/s11604-018-0793-5
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
The "democratization of AI" is progressing, and it is becoming an era when anyone can utilize AI. What kind of radiologists are new generation radiologists suitable for the AI era? The first is maintaining a broad perspective regarding healthcare in its entirety. Next, it is necessary to study the basic knowledge and latest information concerning AI and possess the latest knowledge concerning modalities such as CT/MRI and imaging information systems. Finally, it is important for radiologists to not forget the viewpoint of patient-centered healthcare. It is an urgent task to nurture human resources by realizing such a healthcare AI education program to educate radiologists at an early stage. If we can evolve to become radiologists suitable for the AI era, AI will likely be our ally more than ever and healthcare will progress dramatically. As we approach the "democratization of AI," it is becoming an era in which all radiologists must learn AI as they learn statistics.
引用
收藏
页码：9 / 14
页数：6
相关论文
共 22 条
[1]  
[Anonymous], 2017, ARXIV PREPRINT ARXIV
[2]   Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer [J].
Bejnordi, Babak Ehteshami ;
Veta, Mitko ;
van Diest, Paul Johannes ;
van Ginneken, Bram ;
Karssemeijer, Nico ;
Litjens, Geert ;
van der Laak, Jeroen A. W. M. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2017, 318 (22) :2199-2210
[3]   Dermatologist-level classification of skin cancer with deep neural networks [J].
Esteva, Andre ;
Kuprel, Brett ;
Novoa, Roberto A. ;
Ko, Justin ;
Swetter, Susan M. ;
Blau, Helen M. ;
Thrun, Sebastian .
NATURE, 2017, 542 (7639) :115-+
[4]   Advanced Imaging Techniques in Evaluation of Colorectal Cancer [J].
Garcia-Figueiras, Roberto ;
Baleato-Gonzalez, Sandra ;
Padhani, Anwar R. ;
Luna-Alcala, Antonio ;
Marhuenda, Ana ;
Vilanova, Joan C. ;
Osorio-Vazquez, Iria ;
Martinez-De-Alegria, Anxo ;
Gomez-Caamano, Antonio .
RADIOGRAPHICS, 2018, 38 (03) :740-765
[5]   Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists [J].
Haenssle, H. A. ;
Fink, C. ;
Schneiderbauer, R. ;
Toberer, F. ;
Buhl, T. ;
Blum, A. ;
Kalloo, A. ;
Hassens, A. Ben Hadj ;
Thomas, L. ;
Enk, A. ;
Uhlmann, L. .
ANNALS OF ONCOLOGY, 2018, 29 (08) :1836-1842
[6]   MR-based synthetic CT generation using a deep convolutional neural network method [J].
Han, Xiao .
MEDICAL PHYSICS, 2017, 44 (04) :1408-1419
[7]  
Kunimatsu A, 2018, MAGN RESON MED SCI
[8]   Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks [J].
Lakhani, Paras ;
Sundaram, Baskaran .
RADIOLOGY, 2017, 284 (02) :574-582
[9]   Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs [J].
Larson, David B. ;
Chen, Matthew C. ;
Lungren, Matthew P. ;
Halabi, Safwan S. ;
Stence, Nicholas V. ;
Langlotz, Curtis P. .
RADIOLOGY, 2018, 287 (01) :313-322
[10]   A survey on deep learning in medical image analysis [J].
Litjens, Geert ;
Kooi, Thijs ;
Bejnordi, Babak Ehteshami ;
Setio, Arnaud Arindra Adiyoso ;
Ciompi, Francesco ;
Ghafoorian, Mohsen ;
van der Laak, Jeroen A. W. M. ;
van Ginneken, Bram ;
Sanchez, Clara I. .
MEDICAL IMAGE ANALYSIS, 2017, 42 :60-88
123