AI in Radiology: From Theory into Practice


avatar Erik Ranschaert 1 , *

European Society of Medical Imaging Informatics (EuSoMII), Rotterdam, The Netherlands

how to cite: Ranschaert E. AI in Radiology: From Theory into Practice. I J Radiol. 2019;16(Special Issue):e99303.



Radiology is at the forefront of the revolution in medical imaging, which is mainly based on the progress made in machine learning and deep learning. New tools are being developed and made commercially available for implementation in radiology practice. AI solutions can intervene in different parts of the entire radiological workflow, and thus are likely to have a significant impact on the way that radiology services are being offered.


By listening to this lecture, the audience is expected to:
1. Understand the basic principles of machine learning and deep learning.
2. Understand the different ways and possibilities by which these techniques can be applied in radiology.
3. Understand the advantages, disadvantages, and risks of implementing AI-based tools in radiology practice.


In this presentation, a brief historical overview is provided of the progress that has been made in the past few years in the field of artificial intelligence. The basic principles of machine learning and deep learning are explained. Radiology is at the forefront of these developments, with the ability to provide a huge resource of data. The way these new AI-based applications can be applied is explained, accompanying with advantages, disadvantages, and risks. Advice is provided on how to use these tools in clinical practice.
David marsh
2023-11-10 14:11:44
Great Post! The transition of AI in radiology from theory into practice holds great promise for improving diagnostic accuracy, efficiency, and patient outcomes. However, careful consideration of challenges, ethical implications, and collaborative efforts between healthcare professionals, AI developers, and regulatory bodies is essential for successful integration.