A Deep Learning Model to Predict Breast Cancer Recurrence Using Longitudinal Mammograms and Clinical Data https://doi.org/10.1148/ryai.260941 #AOCMP2026 #medphys #RadPhys #radiology

A deep learning model erases vessels from breast MRI https://doi.org/10.1148/ryai.250630 #AOCMP2026 #medphys #RadPhys #radiology

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #AOCMP2026 #medphys #RadPhys #radiology

Data Resources articles describe important datasets, algorithms, and standards made available to the #AI community https://pubs.rsna.org/page/ai/data_resources #DeepLearning #Radiomics #ArtificialIntelligence

Deep learning models trained on CXRs may exploit exposure parameters as shortcut features; exposure-regimen audits may flag high-risk conditions before clinical deployment https://doi.org/10.1148/ryai.250731 #AOCMP2026 #medphys #RadPhys #radiology

An iterative training approach, the expert-guided annotation loop, for efficient reference standard medical image segmentation https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #CIRSE2026 #IRad #CVRad #radiology

Meet the people leading the science and use of #AI in radiology! Check out our #podcast series on Apple Podcasts, Google Play, or Spotify https://rsnaradiologyai.libsyn.com/ #DeepLearning #Radiomics #ArtificialIntelligence

An algorithmic framework was developed to identify and quantify shortcut learning and bias driven by exposure parameters in chest radiographs, revealing hidden sources of bias in medical artificial intelligence. https://doi.org/10.1148/ryai.250731 #CIRSE2026 #IRad #CVRad #radiology

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge https://doi.org/10.1148/ryai.260179 #CIRSE2026 #IRad #CVRad #radiology

An iterative training approach, the expert-guided annotation loop, for efficient reference standard medical image segmentation https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #CIRSE2026 #IRad #CVRad #radiology

What's new in radiology #AI? Check out The Vasty Deep blog! https://radiologyai.substack.com/ #ML #MachineLearning #Radiomics

An algorithmic framework was developed to identify and quantify shortcut learning and bias driven by exposure parameters in chest radiographs, revealing hidden sources of bias in medical artificial intelligence. https://doi.org/10.1148/ryai.250731 #CIRSE2026 #IRad #CVRad #radiology

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge https://doi.org/10.1148/ryai.260179 #CIRSE2026 #IRad #CVRad #radiology

An expert-guided annotation loop reduced expert annotation time and enabled estimated cost savings while producing high-quality reference standard CT and MRI segmentations https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #CIRSE2026 #IRad #CVRad #radiology

CLAIM: the Checklist for AI in Medical Imaging #guideline #checklist https://rsna.org/claim #AI #ML #DeepLearning

Deep learning models trained on CXRs may exploit exposure parameters as shortcut features; exposure-regimen audits may flag high-risk conditions before clinical deployment https://doi.org/10.1148/ryai.250731 #CIRSE2026 #IRad #CVRad #radiology

Results of the LUNA25 Challenge: AI outperforms radiologists in estimating malignancy risk on indeterminate lung nodules on LDCT https://doi.org/10.1148/ryai.260179 #CIRSE2026 #IRad #CVRad #radiology

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #CIRSE2026 #IRad #CVRad #radiology

Check out our collection of "Data Resources" articles https://pubs.rsna.org/page/ai/data_resources #AI #ML #MachineLearning

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment https://doi.org/10.1148/ryai.250731 #CIRSE2026 #IRad #CVRad #radiology

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #MRI #AI #MachineLearning

Check out today's #FreeFriday @radiology-ai.bsky.social article from #PubMedCentral! MR-Transformer: A Vision Transformer-based Deep Learning Model for Total Knee Replacement Prediction Using MRI https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464714 #AI #ML #DeepLearning

Deep learning models trained on CXRs may exploit exposure parameters as shortcut features; exposure-regimen audits may flag high-risk conditions before clinical deployment https://doi.org/10.1148/ryai.250731 #ChestRad #AI #MachineLearning

An artificial intelligence system outperformed radiologists in malignancy risk estimation of indeterminate lung nodules on baseline CT scans from three European trials, demonstrating its potential as a radiologist decision-support tool. https://doi.org/10.1148/ryai.260179 #Lung #AI #MachineLearning

An iterative training approach, the expert-guided annotation loop, for efficient reference standard medical image segmentation https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #CostAnalysis #CT #AI

#ThrowbackThursday – The #BraTS-Africa Dataset is the first annotated publicly available brain imaging dataset from an #African population (Jun 2025) https://doi.org/10.1148/ryai.240528 #MachineLearning #DeepLearning #ArtificialIntelligence

Deep learning models trained on CXRs may exploit exposure parameters as shortcut features; exposure-regimen audits may flag high-risk conditions before clinical deployment https://doi.org/10.1148/ryai.250731 #ChestRad #ML #MachineLearning

Results of the LUNA25 Challenge: AI outperforms radiologists in estimating malignancy risk on indeterminate lung nodules on LDCT https://doi.org/10.1148/ryai.260179 #AI #ML #MachineLearning

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation https://doi.org/10.1148/ryai.250200 @muellerrom.bsky.social #Segmentation #CostAnalysis #AI

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment https://doi.org/10.1148/ryai.250731 #XRay #RadiologyAI #ML