Domain-Specific AI Application in Medical Imaging: Use Cases


Erik Ranschaert 1 , *

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

How to Cite: Ranschaert E. Domain-Specific AI Application in Medical Imaging: Use Cases, Iran J Radiol. 2019 ; 16(Special Issue):e99306. doi: 10.5812/iranjradiol.99306.


Iranian Journal of Radiology: 16 (Special Issue); e99306
Published Online: December 8, 2019
Article Type: Abstract
Received: November 2, 2019
Accepted: December 8, 2019


Background: In the context of ongoing digitization in healthcare, due to the uprise of machine learning and deep learning, new tools are being developed for implementation in radiology practice. These AI-based applications can be used not only for image analysis in different domains, but also for other parts of the radiological workflow. This will be illustrated with several use cases.

Objectives: By listening to this lecture, the audience is expected to:

1. Understand the basic principles of machine learning and deep learning.

2. Understand the possibilities by which these techniques can intervene in different parts of the radiological workflow.

3. Understand the pathways that need to be followed for developing and implementing AI-based solutions for clinical use.

Outline: AI-based applications can be used for many different purposes in radiology. In each clinical practice, it is essential, however, to define the right use cases for implementing such tools. Furthermore, it is crucial to evaluate the accuracy and value of these tools since the real-world data can be different from the data by which the algorithms are trained. In the Netherlands cancer institute, AI tools are being developed and tested, for both improving patient care and optimizing the radiological workflow. A concise overview is given of the potency of these new tools and different challenges that this project is being confronted with.

Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.