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.

Most used hashtags:

Latest posts

Revolutionising paediatric radiology: the future impact of AI

There remains a hesitancy regarding the adoption of artificial intelligence (AI) within the subspecialties of radiology and whether it has a fixed role in radiology’s future; this is especially true in paediatric radiology. Factors such as a lack of trust in AI applications and a lack of IT infrastructure in many hospitals, among other things, contribute largely to this hesitancy. This

Read More →

Evaluating AI for Clinical Decision-Making: Lessons Learned from a Study of ChatGPT’s Referral Reliability

In our recent study published in European Radiology, we evaluated the reliability of ChatGPT – an AI system developed by OpenAI – as a referral tool for imaging tests, compared to ESR iGuide, a clinical decision support system (CDSS) developed by the European Society of Radiology in cooperation with the American College of Radiology. Four experts served as our ground

Read More →

Deep learning–based identification of spine growth potential on EOS radiographs

In this study, the authors developed a deep learning-based algorithm, which is able to mimic human judgment, in order to help clinicians assess the potential of spine growth based on EOS radiographs. The outcome of the study showed that their deep learning method achieved comparable, and even superior, results compared to those of clinicians, which should have positive applications in

Read More →

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

At present, therapeutic and prognostic recommendations for prostate cancer (PCa) predominantly hinge on risk-stratification tools that are built upon clinical parameters. Recent evidence indicates that incorporating imaging can enhance the precision of prognostic models based on clinical factors. However, challenges like subjective interpretation, variability in image analysis, and the absence of reliable quantitative measures need to be overcome to fully

Read More →

Does deep learning improve the consistency and performance of radiologists in assessing bi-parametric prostate MRI?

Our study aimed to evaluate whether deep learning (DL) software could enhance the consistency and performance of radiologists in assessing bi-parametric prostate MRI scans. Intriguingly, our findings revealed that the DL software did not significantly improve the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency or the detection performance of clinically significant prostate cancer (csPCa) among radiologists with varying levels

Read More →

Are deep models in radiomics performing better than generic models?

In radiomics, deep learning methods are increasingly used because they promise higher predictive performance than models based on generic, hand-crafted features. However, sample sizes in radiomics are often small, and it is known that the performance of deep learning models is often critically dependent on sample size. Therefore, it is unclear whether deep models can outperform generic models. In our

Read More →

AI in thoracic imaging: the transition from research to practice

This commentary explores the use of artificial intelligence tools that are increasingly being implemented into clinical practice within the field of radiology. From using deep learning as a second reader and assistant to radiologists in reading chest x-rays to the financial impact of these tools, the authors look at the adoption of AI in radiology and its usefulness. Article: Artificial intelligence

Read More →

QuantImage v2: physician-centered cloud platform for radiomics and machine learning research

The authors of this study proposed a physician-centered vision of radiomics research to help aid the translation of radiomics prediction models into clinical practice and workflow. Free-to-access radiomics tools and frameworks were reviewed to help identify best practices and gaps in the existing software. The authors designed QuantImage V2 (QI2) to help implement this vision and address any issues they

Read More →

A deep learning model using chest X-ray to identify TB and NTM-LD patients

The authors of this study aimed to evaluate whether artificial intelligence, specifically a deep neural network (DNN), was able to distinguish between tuberculosis (TB) or nontuberculous mycobacterial lung disease (NTM-LD) patients through chest X-rays (CXRs) from suspected mycobacterial lung disease. A total of 1,500 CXRs from two hospitals were retrospectively collected and evaluated. They determined that the developed DNN model

Read More →

Evaluating the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

In the ever-evolving landscape of radiology, the quest for enhanced image quality and reduced noise, particularly in obese patients, remains an enduring challenge. It is within this context that a novel algorithm for noise reduction in dual-source dual-energy (DE) CT imaging is a promising development. In a retrospective study involving seventy-nine patients with contrast-enhanced abdominal imaging, this novel algorithm was

Read More →

Become A Member Today!

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. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.


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

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.

The membership type best fitting for you will be selected automatically during the application process.



Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

Not all activities included
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
European Radiology, Insights into Imaging, European Radiology Experimental.