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Published in: Japanese Journal of Radiology 1/2019

01-01-2019 | Invited Review

How will “democratization of artificial intelligence” change the future of radiologists?

Authors: Yasuyuki Kobayashi, Maki Ishibashi, Hitomi Kobayashi

Published in: Japanese Journal of Radiology | Issue 1/2019

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Abstract

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.
Literature
3.
go back to reference Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.CrossRefPubMed Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.CrossRefPubMed
4.
go back to reference Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–118. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–118.
5.
go back to reference Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine. diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29:1836–42. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine. diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29:1836–42.
6.
go back to reference Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318:2199–210.CrossRefPubMedPubMedCentral Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318:2199–210.CrossRefPubMedPubMedCentral
7.
go back to reference Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado SG, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2018;2:158–64.CrossRefPubMed Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado SG, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2018;2:158–64.CrossRefPubMed
9.
go back to reference Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017;284:574–82.CrossRefPubMed Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017;284:574–82.CrossRefPubMed
10.
go back to reference Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. 2017.arXiv:1711.05225. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. 2017.arXiv:1711.05225.
11.
go back to reference Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology. 2017;285:923–31.CrossRefPubMed Prevedello LM, Erdal BS, Ryu JL, Little KJ, Demirer M, Qian S, et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology. 2017;285:923–31.CrossRefPubMed
12.
go back to reference Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. 2018;24:1337–41.CrossRefPubMed Titano JJ, Badgeley M, Schefflein J, Pain M, Su A, Cai M, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. 2018;24:1337–41.CrossRefPubMed
13.
go back to reference Nakao T, Hanaoka S, Nomura Y, Sato I, Nemoto M, Miki S, et al. Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J Magn Reson Imaging. 2018;47:948–53.CrossRefPubMed Nakao T, Hanaoka S, Nomura Y, Sato I, Nemoto M, Miki S, et al. Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J Magn Reson Imaging. 2018;47:948–53.CrossRefPubMed
14.
go back to reference Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287:313–22.CrossRefPubMed Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287:313–22.CrossRefPubMed
16.
go back to reference Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology. 2018;286:887–96.CrossRefPubMed Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology. 2018;286:887–96.CrossRefPubMed
17.
go back to reference Trivedi H, Mesterhazy J, Laguna B, Vu T, Sohn JH. Automatic determination of the need for intravenous contrast in musculoskeletal MRI examinations using IBM Watson's natural language processing algorithm. J Digit Imaging. 2018;31:245–51.CrossRefPubMed Trivedi H, Mesterhazy J, Laguna B, Vu T, Sohn JH. Automatic determination of the need for intravenous contrast in musculoskeletal MRI examinations using IBM Watson's natural language processing algorithm. J Digit Imaging. 2018;31:245–51.CrossRefPubMed
21.
go back to reference Han X. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017;44:1408–19.CrossRefPubMed Han X. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017;44:1408–19.CrossRefPubMed
22.
go back to reference Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018;36:257–72.CrossRefPubMed Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Deep learning with convolutional neural network in radiology. Jpn J Radiol. 2018;36:257–72.CrossRefPubMed
23.
go back to reference García-Figueiras R, Baleato-González S, Padhani AR, Luna-Alcalá A, Marhuenda A, Vilanova JC, et al. Advanced imaging techniques in evaluation of colorectal cancer. Radiographics. 2018;38:740–65.CrossRefPubMed García-Figueiras R, Baleato-González S, Padhani AR, Luna-Alcalá A, Marhuenda A, Vilanova JC, et al. Advanced imaging techniques in evaluation of colorectal cancer. Radiographics. 2018;38:740–65.CrossRefPubMed
25.
go back to reference Nakajima Y, Yamada K, Imamura K, Kobayashi K. Radiologist supply and workload: international comparison. Working Group of Japanese College of Radiology. Radiat Med. 2008;26:455–65. Nakajima Y, Yamada K, Imamura K, Kobayashi K. Radiologist supply and workload: international comparison. Working Group of Japanese College of Radiology. Radiat Med. 2008;26:455–65.
Metadata
Title
How will “democratization of artificial intelligence” change the future of radiologists?
Authors
Yasuyuki Kobayashi
Maki Ishibashi
Hitomi Kobayashi
Publication date
01-01-2019
Publisher
Springer Japan
Published in
Japanese Journal of Radiology / Issue 1/2019
Print ISSN: 1867-1071
Electronic ISSN: 1867-108X
DOI
https://doi.org/10.1007/s11604-018-0793-5

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