AI Blog

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

The Rise of Augmented Radiology as an enabler to achieve Precision Health

The field of medical imaging has witnessed a revolution thanks to the digital transformation, innovation and availability of advanced clinical applications. New imaging techniques are helping radiologists, oncologists, and other diagnosticians with greater anatomical and clinical details, highlighting the need for fast access to imaging reports and results, evidence-based collaborative peer review and collaboration, and predictive intelligence. A pioneer in

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Convolutional neural networks: an overview and application in radiology

Numerous domains, including radiology, have shown interest in convolutional neural network (CNN) – a class of artificial neural networks that has become dominant in various computer vision tasks. It is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article

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Treat artificial intelligence as disruptive technology

Radiologists tend to be afraid of artificial intelligence (AI), but they should demystify it and take it for what it is, i.e. disruptive technology, according to Frank Lexa, a professor of radiology at the University of Arizona and an expert in medical leadership. “AI tools improve at the speed of light, and it’s unclear whether radiologists will keep on playing

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Big data, artificial intelligence, and structured reporting

Imagine you wanted to teach a baby to differentiate a ball from a dog. How would you do that? Intuitively it would make sense to repeatedly point at them while naming each object accordingly. But how about, e.g. if instead of saying “dog” or “ball”, you would only name the specific breed of dog without using the word “dog”? Maybe

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A survey of medical students’ attitude toward AI

The aim of this survey was to assess undergraduate medical students’ attitudes towards artificial intelligence (AI) in radiology and medicine. A web-based questionnaire consisting of various sections aiming to evaluate the students’ prior knowledge of AI in radiology and beyond was sent out to students at three major medical schools. The survey showed that undergraduate medical students do not worry

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AI for CT Image Reconstruction – A Great Opportunity

It seems like everybody in the radiology field is now working with Artificial Intelligence (AI) in one way or another. Especially here in the Silicon Valley, the number of ventures that develop AI algorithms is increasing by the day. What are these newly developed algorithms focusing on? Most of them try to solve a specific problem, and they do that

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Radiomics of liver MRI predict metastases in mice

This study aimed to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye. For the purposes of the study, eight mice were injected intraportally with syngeneic MC-38 colon cancer cells and two mice were injected with phosphate-buffered saline (sham controls). Magnetic resonance imaging (MRI) and texture analysis were

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Comparing computer- and human-extracted imaging phenotypes

This retrospective study sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. Upon obtaining the results, it was possible to conclude that quantitative radiomics of breast cancer may replicate human-extracted tumour size

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Demystification of AI-driven Medical Image Interpretation

This article provides an overview of the history of AI methods for radiological image analysis with the aim to deliver a context for the latest developments. The functioning, strengths and limitations of more classical methods and the more recent Deep Learning techniques are reviewed. In order to highlight the potential of AI in radiology, but also the constraints of certain AI

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How will Artificial Intelligence impact the field of radiology?

The topic of artificial intelligence (AI) has become one of the main points of discussion in the field of radiology and medicine. Through discussions on various cases of the use of AI in healthcare, the importance of training radiologists in emerging technologies, and potential threats to jobs, the authors develop an overall picture of how AI fits into the current

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You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

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for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

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Reduced registration fees for ECR 2024:
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