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    3. Algorithms: All Set to Shape Tomorrow’s Medicine

    Algorithms: All Set to Shape Tomorrow’s Medicine

    6min
    Philipp Grätzel von Grätz
    Published on July 23, 2019

    Deep learning in medicine can handle time consuming activities and therefore reduce the radiologists’ workload.

    Bram Stieltjes, MD, Head of Research Coordination, Radiology & Nuclear Medicine Clinic, University Hospital Basel, Switzerland

    Practical uses for AI thanks to increased computing power

    Bram Stieltjes, MD, Head of Research Coordination, Radiology & Nuclear Medicine Clinic, University Hospital Basel, Switzerland

    “Mathematical revolution in radiology”

    It’s not at all clear whether we are looking at images or at purely statistical parameters.

    Professor Stefan Schönberg, Chairman, Department of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany

    Most radiologists don’t see AI as a threat but as something beneficial to the field.

    Professor Michael Forsting,Institute of Diagnostic and Interventional Radiology and Neuroradiology, Essen University Hospital, Germany

    AI to make inroads into labs

    The first step will definitely be an increase in efficiency thanks to AI.

    Professor Frederick Klauschen, Institute of Pathology, Charité, Berlin, Germany

    Will diagnostic specialists become redundant?

    Through constant neural network training, machine learning medicine is becoming more and more involved in daily clinical routines.

    AI and narrative-based medicine

    By Philipp Grätzel von Grätz

    Philipp Grätzel von Grätz lebt und arbeitet als freiberuflicher Medizinjournalist in Berlin. Seine Spezialgebiete sind Digitalisierung, Technik und Herz-Kreislauf-Therapie.

    1. [1] From Automating Muscle to Augmenting the Brain. How Artificial Intelligence Will Change the Clinical Laboratory. Siemens Healthineers/Siemens Healthcare Diagnostics Inc., Tarrytown, NY, July 2018. https://www.siemens-healthineers.com/news/mso-ai-willchange-clinical-laboratory.html. Last accessed May 14th, 2019
    2. [2] Nat Med. 2019 Jan;25(1):60-64
    3. 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.