Bits and bytes: the future of radiology lies in informatics and information technology


Advances in informatics and information technology are sure to alter the practice of medical imaging and image-guided therapies substantially over the next decade. Each element of the imaging continuum will be affected by substantial increases in computing capacity coincident with the seamless integration of digital technology into our society at large. This article focuses primarily on areas where this IT transformation is likely to have a profound effect on the practice of radiology.

Key points

Clinical decision support ensures consistent and appropriate resource utilization.

Big data enables correlation of health information across multiple domains.

Data mining advances the quality of medical decision-making.

Business analytics allow radiologists to maximize the benefits of imaging resources.

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  1. 1.

    Sistrom CL, Dang PA, Weilburg JB, Dreyer KJ, Rosenthal DI, Thrall JH (2009) Effect of computerized order entry with integrated decision support on the growth of outpatient procedure volumes: seven year time series analysis. Radiology 251:147–155

    Article  PubMed  Google Scholar 

  2. 2.

    Ip IK, Schneider L, Seltzer S et al (2013) Impact of provider-led, technology-enabled radiology management program on imaging. Am J Med 126:687–692

    Article  PubMed  Google Scholar 

  3. 3.

    Personal communication, Robert Cooke, National Decision Support Company, on October 12, 2016

  4. 4.

    Boland GWL, Thrall JH, Gazelle GS et al (2011) Decision support for radiologist report recommendations. JACR 8:819–823

    PubMed  Google Scholar 

  5. 5.

    Lu MT, Rosman DA, Wu CC et al (2016) Radiologist point-of-care clinical decision support and adherence to guidelines for incidental lung nodules. JACR 13:156–162

    PubMed  Google Scholar 

  6. 6.

    O’Reilly KB (2015) Integrating AP and radiology, inch by inch. CAP Today. Accessed 29 June 2016

  7. 7.

    Gantz J, Reinsel D (2010) The digital universe decade – are you ready? Accessed 30 April 2016)

  8. 8.

    Institute for Health Technology Transformation. Transforming health care through big data strategies for leveraging big data in the health care industry (

  9. 9.

    Jacobs A (2009) The pathologies of big data. ACM Queue 7:1–12

    Article  Google Scholar 

  10. 10.

    Yim WW, Yetisgen M, Harris WP, Kwan SW (2016) Natural language processing in oncology: a review. JAMA Oncol 2:797–804

    Article  PubMed  Google Scholar 

  11. 11.

    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  13. 13.

    Cook TS, Nagy P (2014) Business intelligence for the radiologist: making your data work for you. J Am Coll Radiol 11:1238–1240

    Article  PubMed  Google Scholar 

  14. 14.

    Maris B. Medicine’s transistor moment: 8 emerging technologies that could revolutionize the life sciences. Google Ventures Library Web site. Published May 26, 2015. Accessed 23 June 2016

  15. 15.

    Davies A (2016) Google’s self-driving car caused its first crash. Wired. Accessed 23 June 2016

  16. 16.

    Google self-driving car project Web site. Monthly reports. Accessed 23 June 2016

  17. 17.

    Best J (2013) IBM Watson: The inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next. TechRepublic. Accessed 23 June 2016

  18. 18.

    Koch C, Tononi G (2011) A test for consciousness. Sci Am 304:44–47

    Article  PubMed  Google Scholar 

  19. 19.

    The internet of things (IoT) in health care. Research brief, The Advisory Board Company. April 8, 2015

  20. 20.

    Evans D (2011) The internet of things: how the next evolution of the internet is changing everything. Cisco Internet Business Solutions Group White Paper

  21. 21.

    Desilver D (2014) Chart of the Week: The ever-accelerating rate of technology adoption. PewResearchCenter Web site. Accessed 23 June 2016

  22. 22.

    Broadband Blues (2001) The Economist. Accessed 23 June 2016

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We would like to thank members of the International Society for Strategic Studies in Radiology (IS3R) for contributing to the discussion of the above topics. We would also like to thank Hedi Hricak for her review and helpful comments and Ada Muellner for her editing. The scientific guarantor of this publication is James A. Brink, MD. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional review board approval was not required because no research was conducted.

Presented at the biannual meeting of: International Society of Strategic Studies in Radiology, Amsterdam, The Netherlands, August 28, 2015.

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Correspondence to James A. Brink.

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Brink, J.A., Arenson, R.L., Grist, T.M. et al. Bits and bytes: the future of radiology lies in informatics and information technology. Eur Radiol 27, 3647–3651 (2017).

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  • Medical informatics
  • Information technology
  • Clinical decision support
  • Data mining
  • Artificial intelligence