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Visit this page for more information about our journals: European Radiology, Insights into Imaging, and European Radiology Experimental, as well as information about Eurorad. Additionally, if you’re interested in publishing your own work, you can find details here.

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Back to main AI Blog

Featured

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:

deep learningmachine learningmriradiomicsx-ray computed tomographyinterviewai newsmammographyCOVID-19neural networksdiagnostic imagingbreast cancerworkflowtomographybreast neoplasmsradiography

Latest posts

Label-set impact on deep learning-based prostate segmentation on MRI

Jakob Meglič January 2024

This study delves into a less-explored territory in automatic prostate segmentation: label-set selection. Recognizing the emphasis on dataset selection in segmentation model training, we thought it crucial to investigate the impact of the labels, i.e. the manual segmentations, on model performance. Although label sets are often considered the gold standard, as they are provided by highly trained professionals, disparities emerge

Read More →

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

European Radiology January 2024

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Article: External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

Read More →

ChatGPT makes medicine easy to swallow

Katharina Jeblick December 2023

Just over a year ago, the release of ChatGPT marked a turning point in AI and language processing, sparking widespread excitement for Large Language Models (LLMs) for diverse use cases across various domains. Back then, we noticed friends using ChatGPT for medical text simplification, yet, as medical laypersons, they couldn’t verify the accuracy of the simplified text. Anticipating its imminent adoption by patients,

Read More →

Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

Insights into Imaging December 2023

In this study, the utilization of a convolutional neural network (CNN)-based arterial input function (AIF) and whether it improves the volumetric estimation of core penumbra was investigated. The authors included 160 acute ischemic stroke patients for this study, who underwent CTP imaging, National Institutes of Health Stroke Scale (NIHSS), and Alberta Stroke Programme Early CT Score (APSECTS) grading. They were

Read More →

Revolutionising paediatric radiology: the future impact of AI

European Radiology December 2023

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

Mor Saban December 2023

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

European Radiology November 2023

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?

Andrea Ponsiglione November 2023

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?

Deniz Alis November 2023

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?

Aydin Demircioğlu October 2023

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 →

Label-set impact on deep learning-based prostate segmentation on MRI

Jakob Meglič, Mohammed R. S. Sunoqrot, Tone Frost Bathen, Mattijs Elschot January 2024

This study delves into a less-explored territory in automatic prostate segmentation: label-set selection. Recognizing the emphasis on dataset selection in segmentation model training, we thought it crucial to investigate the impact of the labels, i.e. the manual segmentations, on model performance. Although label sets are often considered the gold standard, as they are provided by highly trained professionals, disparities emerge

Read More →

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

European Radiology January 2024

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Article: External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

Read More →

ChatGPT makes medicine easy to swallow

Katharina Jeblick, Balthasar Schachtner December 2023

Just over a year ago, the release of ChatGPT marked a turning point in AI and language processing, sparking widespread excitement for Large Language Models (LLMs) for diverse use cases across various domains. Back then, we noticed friends using ChatGPT for medical text simplification, yet, as medical laypersons, they couldn’t verify the accuracy of the simplified text. Anticipating its imminent adoption by patients,

Read More →

Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

Insights into Imaging December 2023

In this study, the utilization of a convolutional neural network (CNN)-based arterial input function (AIF) and whether it improves the volumetric estimation of core penumbra was investigated. The authors included 160 acute ischemic stroke patients for this study, who underwent CTP imaging, National Institutes of Health Stroke Scale (NIHSS), and Alberta Stroke Programme Early CT Score (APSECTS) grading. They were

Read More →

Revolutionising paediatric radiology: the future impact of AI

European Radiology December 2023

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

Mor Saban December 2023

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

European Radiology November 2023

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?

Andrea Ponsiglione November 2023

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?

Deniz Alis November 2023

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?

Aydin Demircioğlu October 2023

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 →

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.

①

Membership

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
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 
  • Content e-mails for all ESR journals4
  • 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.
②

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
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 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.
③

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.
Apply now

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

Footnotes:

01

Reduced registration fees for ECR 2026:
Provided that ESR 2025 membership is activated and approved by August 31, 2025.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.