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    3. The Changing Role of the Radiologist in the Age of AI

    The Changing Role of the Radiologist in the Age of AI

    4min
    Martin Lindner
    Published on November 8, 2018
    Digitalization and artificial intelligence change the role of the radiologist.

    Automated image interpretation could prove an indispensable aid to cope with increasing workloads.

    The role of radiologists in the clinical and scientific activities will be changed through digitalization.

    Radiologists will work in a far more interdisciplinary way in their clinical and scientific activities than before.

    “I am convinced that AI is not a threat but a tool that we can use to support our work and to improve results.”

    Bernd Hamm, President, European Congress of Radiology 2018

    AI will be a tool for radiologists, not a threat

    “It is not clear if we are still looking at images or at statistical parameters.”

    Stefan Schönberg, University Hospital Mannheim, Germany

    Embracing a mathematical mindset

    Strategic steps to take

    Simplified schematic of the diagnostic radiology workflow, with examples of where AI systems can be implemented. Source: drhughharvey

    “If radiologists have to learn about the physics of MRI, then I think learning about the basic principles of imaging informatics is at least as important.”

    Sergey Morozov, President, European Society of Medical Imaging Informatics

    Related Links

    By Martin Lindner

    Martin Lindneris is an award-winning science writer based in Berlin, Germany. After completing his medical studies and a doctoral thesis in the history of medicine, he went into journalism. His articles have appeared in many major German and Swiss newspapers and magazines.

    1. All online sources last accessed Sept. 27th, 2018
    2. [1] Thrall JH, Li X, Li Q, et al. (2018) Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol 15:504-508
    3. [2] Michael Walter (March 13, 2018) Are European radiologists skeptical about AI? A report from ECR 2018, www.radiologybusiness.com/topics/artificial-intelligence/are-european-radiologists-skeptical-about-ai-report-ecr-2018
    4. [3] Philip Ward et al. (March 8, 2018) Top 5 trends from ECR 2018 in Vienna, www.auntminnieeurope.com/index.aspx?sec=rca&sub=ecr_2018&pag=dis&itemid=615650
    5. [4] McDonald RJ, Schwartz KM, Eckel LJ, et al. (2015) The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol 22:1191-8
    6. [5] Hainc N, Federau C, Stieltjes B, et al. (2017) The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading. Front Neurol 8:489
    7. [6] Aerts HJ, Velazquez ER, Leijenaar RT, et al. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006
    8. [7] Hugh Harvey (January 24, 2018) Why AI will not replace radiologists, https://towardsdatascience.com/why-ai-will-not-replace-radiologists-c7736f2c7d80
    9. [8] Yu KH, Zhang C, Berry GJ, et al. (2016) Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 7:12474
    10. [9] Tang A, Tam R, Cadrin-Chênevert A, et al. (2018) Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Can Assoc Radiol J 69:120-135
    11. [10] www.eusomii.pro/newsletter-2018/#medicalimaginginformatics
    12. [11] www.myesr.org/education/training-curricula
    13. The statements by Siemens Healthineers customers described herein are based on results that were achieved in the customer’s unique setting. Since there is no “typical” hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.