IJ Radiology
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.
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.
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 (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
Ranschaert E. AI in Radiology: From Theory into Practice. I J Radiol. 2019;16(Special Issue):e99303. doi: https://doi.org/10.5812/iranjradiol.99303
Tahaei MS. AI Startups: The Need for Collaborative Research and Development. I J Radiol. 2019;16(Special Issue):e99304. doi: https://doi.org/10.5812/iranjradiol.99304
Fatehi M. Collaborative AI Research in Medical Imaging: Trends and Challenges. I J Radiol. 2019;16(Special Issue):e99222. doi: https://doi.org/10.5812/iranjradiol.99222
Eren Güzel H, Bozer A. Artificial Intelligence Adoption and Perspectives Among Turkish Radiologists: A National Survey. I J Radiol. 2025;22(3):e165130. doi: https://doi.org/10.5812/iranjradiol-165130
Fatehi M. Referral Guidelines in Radiology: Point of Care Advice. I J Radiol. 2019;16(Special Issue):e99225. doi: https://doi.org/10.5812/iranjradiol.99225
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