Japanese Journal of Radiology

, Volume 37, Issue 1, pp 9–14 | Cite as

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

  • Yasuyuki KobayashiEmail author
  • Maki Ishibashi
  • Hitomi Kobayashi
Invited Review


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.


Democratization Artificial Intelligence Medicine Radiology Radiologist 


Compliance with ethical standards

Conflict of interest

YK has received a research grant from Canon Medical Systems. MI and HK has no conflict of interest.


  1. 1.
  2. 2.
  3. 3.
    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.CrossRefGoogle Scholar
  4. 4.
    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.Google Scholar
  5. 5.
    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.Google Scholar
  6. 6.
    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.CrossRefGoogle Scholar
  7. 7.
    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.CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017;284:574–82.CrossRefGoogle Scholar
  10. 10.
    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.Google Scholar
  11. 11.
    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.CrossRefGoogle Scholar
  12. 12.
    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.CrossRefGoogle Scholar
  13. 13.
    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.CrossRefGoogle Scholar
  14. 14.
    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.CrossRefGoogle Scholar
  15. 15.
    Kunimatsu A, Kunimatsu N, Yasaka K, Akai H, Kamiya K, Watadani T, et al. Machine learning-based texture analysis of contrast-enhanced MR Imaging to differentiate between glioblastoma and primary central nervous system lymphoma. Magn Reson Med Sci. 2018. Scholar
  16. 16.
    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.CrossRefGoogle Scholar
  17. 17.
    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.CrossRefGoogle Scholar
  18. 18.
    Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol. 2018. Scholar
  19. 19.
    Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 555(7697):487-492.
  20. 20.
    Noguchi T, Higa D, Asada T, Kawata Y, Machitori A, Shida Y, et al. Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences. Jpn J Radiol. 2018. Scholar
  21. 21.
    Han X. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017;44:1408–19.CrossRefGoogle Scholar
  22. 22.
    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.CrossRefGoogle Scholar
  23. 23.
    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.CrossRefGoogle Scholar
  24. 24.
  25. 25.
    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.Google Scholar
  26. 26.
    Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216–9.CrossRefGoogle Scholar

Copyright information

© Japan Radiological Society 2018

Authors and Affiliations

  • Yasuyuki Kobayashi
    • 1
    Email author
  • Maki Ishibashi
    • 1
  • Hitomi Kobayashi
    • 2
  1. 1.Department of Medical Information and Communication Technology Research, Graduate School of MedicineSt. Marianna University School of MedicineKawasakiJapan
  2. 2.Division of Hematology and Rheumatology, Department of MedicineNihon University School of MedicineTokyoJapan

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