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

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations

Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a 45% fall in effective dose and a halving of lifetime attributable cancer risk without sacrificing diagnostic confidence. The message is simple: When the image quality can be algorithmically recovered, radiologists are no longer forced to

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ChatGPT-4o powers AI-generated simplified breast imaging reports for enhanced patient comprehension

In our study, we evaluated ChatGPT-4o’s ability to simplify breast imaging reports while maintaining accuracy and completeness. Radiology reports often contain complex terminology, creating barriers to patient understanding. We aimed to assess whether AI-driven simplifications could enhance clarity without introducing errors. Our results show that AI-generated summaries maintained high factual accuracy (median score: 2/5) and completeness (2/5), with a low

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How to get to valuable radiology AI: the role of early health technology assessment

Artificial intelligence (AI) technologies, such as large language and deep learning models, are poised to significantly influence clinical practice. However, the real-world impact and utility of these AI applications, particularly in radiology, often remain unclear. How can their impact be measured, and can we ensure it benefits everyone? In ESR Essentials: how to get to valuable radiology AI: the role

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Impact of uncertainty quantification through conformal prediction on volume assessment from DL-based MRI prostate segmentation

This study aimed to evaluate the uncertainty of a deep learning (DL)-based prostate segmentation algorithm using conformal prediction (CP) and its impact on prostate volume (PV) calculation in patients at risk of prostate cancer (PC). The study involved 377 patients’ 3-Tesla T2-weighted scans. By applying CP at an 85% confidence level, unreliable pixel segmentations of the DL model were flagged,

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This blur detection model could provide instantaneous feedback to technicians

This study developed a model to automatically detect blurred areas in mammograms, which can affect diagnostic accuracy. Using a retrospective dataset consisting of 152 mammograms from three vendors, expert radiologists outlined blurred regions. Normalized Wiener spectra (nWS) were extracted and processed through a convolutional neural network (CNN) to classify images as either blurred or sharp. The model showed an AUROC

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On Artificial Intelligence: An interview with Gennaro D’Anna

The latest addition to our “On Artificial Intelligence” series finds us diving into artificial intelligence (AI) within the world of radiology and, more specifically, neuroradiology. Gennaro D’Anna, a neuroradiologist at CDI Centro Diagnostico Italiano in Milan, Italy, discusses challenges to adoption in clinical practice, what the future looks like for AI, and what new generations of radiologists can do to

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AI and machine learning-based diagnostic advantages and pitfalls: Musculoskeletal fracture detection

Artificial intelligence (AI) is increasingly integrated into healthcare, especially within diagnostic radiology. AI algorithms, particularly those utilizing machine learning and computer vision, are widely used for evaluating MRI, CT, and radiographic images. These models, trained on extensive patient databases, are now fully commercialized. Ongoing research compares AI’s efficacy to human radiologists in diagnosing various pathologies, such as scoliosis, hip dysplasia,

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Avoiding Radiology’s Boeing 737 Max moment

Recently, Kemper et al. published an insightful paper on the challenges and opportunities of health technology assessment (HTA) models to evaluate the value, safety, and trustworthiness of radiology computer vision AI (RCVAI) tools used in daily practice. Formal HTA frameworks are theoretically accurate and help define complex issues, but they are ideal approaches. The real world is messier. Ideal descriptions

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Image biomarkers and explainable AI

Feature extraction and selection in medical data are crucial for radiomics and image biomarker discovery, particularly using convolutional neural networks (CNNs). The process involves feature extraction, dimensionality reduction, and addressing the curse of dimensionality. While deep learning (DL) techniques perform well, handcrafted features are important for certain studies and need to be considered. Dataset size and diversity are also key

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On Artificial Intelligence: An interview with Daniel Truhn

Following a fellowship in artificial intelligence (AI) at the Institute of Imaging and Computer Vision at RWTH Aachen, which only solidified his belief in AI’s great potential in medicine, Daniel Truhn, a Diagnostic and Interventional radiologist at University Hospital Aachen in Germany, started working on AI’s application in medicine, even creating his own working group in the area. Join us

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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.

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

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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

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

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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.