A new commentary discusses the value of using random convolutions to bridge the gap across medical imaging domains https://doi.org/10.1148/ryai.251014 #ASPNR26 #PedsRad #NeuroRad #radiology

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset https://doi.org/10.1148/ryai.250480 @utah.edu #ASPNR26 #PedsRad #NeuroRad #radiology

Why AI should measure, not just detect https://radiologyai.substack.com/p/why-ai-should-measure-not-just-detect #ASPNR26 #PedsRad #NeuroRad #radiology

Check out today's #FreeFriday @radiology-ai.bsky.social article from #PubMedCentral! WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605144#FreeFriday#PubMedCentral!#MachineLearning #DeepLearning #Radiomics

What's next after LLMs? AI agents that support whole radiology workflows! https://doi.org/10.1148/ryai.250651 #ASPNR26 #PedsRad #NeuroRad #radiology

Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models https://doi.org/10.1148/ryai.240502 @tum.de #ASPNR26 #PedsRad #NeuroRad #radiology

Introducing Dr. Merel Huisman https://radiologyai.substack.com/p/introducing-dr-merel-huisman #blogging #AI #MachineLearning

New commentary! Random convolutions for improving segmentation across different medical imaging domains https://doi.org/10.1148/ryai.251014 #segmentation #DeepLearning #MachineLearning

Introducing the LumbarDISC Dataset, the largest publicly available adult MRI lumbar spine dataset for degenerative disease https://doi.org/10.1148/ryai.250480 @utah.edu #spine #MSKRad #ML

In new Vasty Deep post, Keno Bressem discusses the importance of AI measurement tools for radiology https://radiologyai.substack.com/p/why-ai-should-measure-not-just-detect #blog #radiology #MachineLearning

#ThrowbackThursday – #OpenSource #DeepLearning model detects & localizes breast cancer on MRI (Aug 2025) https://doi.org/10.1148/ryai.240550#ThrowbackThursday#OpenSource#DeepLearning#ML #MachineLearning #ArtificialIntelligence

A new narrative review describes the evolution of AI applications for radiology from LLMs to autonomous agents https://doi.org/10.1148/ryai.250651 #agent #LLM #MachineLearning

Random Convolutions for Domain Generalization of Deep Learning-based Medical Image Segmentation Models https://doi.org/10.1148/ryai.240502 @tum.de #segmentation #DeepLearning #MachineLearning

Dr. Merel Huisman joins the Editorial Board of Radiology: Artificial Intelligence https://radiologyai.substack.com/p/introducing-dr-merel-huisman #radiology #ArtificialIntelligence #ML

New commentary! Random convolutions for improving segmentation across different medical imaging domains https://doi.org/10.1148/ryai.251014 #segmentation #AI #MachineLearning

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset https://doi.org/10.1148/ryai.250480 @utah.edu #spine #AI #ML

A #DeepLearning MRI denoising method using signal-to-noise ratio unit reconstruction and g-factor augmentation to analyze noise distributions https://doi.org/10.1148/ryai.250227#DeepLearning@ucl.ac.uk #medphys #denoising #radiology

Check out our collection of articles on "Mitigating Bias" from @MayoAILab @Slowvak https://pubs.rsna.org/page/ai/mitigating_bias #ML #DeepLearning #Radiomics

A #DeepLearning framework synthesizes anatomically accurate infant brain MRIs, outperforming competing methods in image quality and tissue segmentation ​ https://doi.org/10.1148/ryai.240708#DeepLearning #NeuroRad #MRI #AI

AI-CAD interpretation of mammograms collected in the AI-STREAM Trial: cancers missed and detected https://doi.org/10.1148/ryai.250281 #BreastCancer #mammo #MachineLearning

Extending brain extraction tools beyond non-enhanced CT https://doi.org/10.1148/ryai.240847 #brain #AI #MachineLearning

New Special Report! Methods for preparing imaging data for AI while protecting privacy https://doi.org/10.1148/ryai.250273 #FederatedLearning #deID #ML

Establishing normative, age-based, pediatric kidney length and volume percentiles from a large US dataset with a natural language processing model https://doi.org/10.1148/ryai.250056 #kidney #PedsRad #USRad

Gastric Neoplasm Detection on Contrast-enhanced CT with Deep Learning https://doi.org/10.1148/ryai.250145 #cancer #segmentation #radiology

Join hosts Paul Yi & @alitejanimd.bsky.social for the Radiology: Artificial Intelligence #podcast series! @rsnasky.bsky.social https://rsnaradiologyai.libsyn.com/#podcast#AI #DeepLearning #ArtificialIntelligence

A new MRI denoising method, SNRAware, improves SNR in analyses of phantoms, an internal test set, and four external test sets https://doi.org/10.1148/ryai.250227 @ucl.ac.uk #medphys #CVRad #ML

Economic Value of AI in Radiology: A Systematic Review https://doi.org/10.1148/ryai.250090 @tum.de @unibas.ch #radiology #ArtificialIntelligence #ML

Giouroukou et al. review imaging data preparation for AI use: methods for protecting patient privacy https://doi.org/10.1148/ryai.250273 #deID #deidentification #ML

CTA-BET outperforms five benchmark non-commercial tools in brain mask generation on non-enhanced CT and CT angiography https://doi.org/10.1148/ryai.240847 #NeuroRad #AI #ML

Gastric Neoplasm Detection on Contrast-enhanced CT with Deep Learning https://doi.org/10.1148/ryai.250145 #AbdRad #AI #MachineLearning

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

#Mirai risk model identifies mammographic observations not explicitly defined during training https://doi.org/10.1148/ryai.240417#Mirai #BreastRad #cancer #MachineLearning

AI-CAD interpretation of mammograms collected in the AI-STREAM Trial: cancers missed and detected https://doi.org/10.1148/ryai.250281 #BreastCancer #mammography #AI

Economic Value of AI in Radiology: A Systematic Review https://doi.org/10.1148/ryai.250090 @tum.de @unibas.ch #radiology #ArtificialIntelligence #ML

US-derived Pediatric Kidney Length and Volume Percentiles by Age: A Big Data Approach https://doi.org/10.1148/ryai.250056 #NLP #AI #MachineLearning

Giouroukou et al. review imaging data preparation for AI use: methods for protecting patient privacy https://doi.org/10.1148/ryai.250273 #FederatedLearning #AI #deID

Understanding how mammography acquisition parameters affect #AI and radiologist performance is crucial for safe clinical AI integration https://doi.org/10.1148/ryai.240861#AI@danafarber.bsky.social #Radiology #BreastCancer #breast

The Checklist for AI in Medical Imaging addresses #AI applications that include classification, segmentation, and reconstruction of medical images https://pubs.rsna.org/page/ai/claim#AI#ML #MachineLearning #ArtificialIntelligence

A brain extraction tool for both non-enhanced CT and CT angiography: CTA-BET https://doi.org/10.1148/ryai.240847 #nnUnet #ML #MachineLearning

A new MRI denoising method, SNRAware, improves SNR in analyses of phantoms, an internal test set, and four external test sets https://doi.org/10.1148/ryai.250227 @ucl.ac.uk #DeepLearning #radiology #CardiacCine

Gastric Neoplasm Detection with AI (GANDA) can detect and diagnose gastric neoplasm on contrast-enhanced CT https://doi.org/10.1148/ryai.250145 #cancer #ML #MachineLearning

Establishing normative, age-based, pediatric kidney length and volume percentiles from a large US dataset with a natural language processing model https://doi.org/10.1148/ryai.250056 #NLP #PedsRad #ML

New Special Report! Methods for preparing imaging data for AI while protecting privacy https://doi.org/10.1148/ryai.250273 #privacy #FederatedLearning #ML

Breast Cancers Detected and Missed by AI-CAD: Results from the AI-STREAM Trial https://doi.org/10.1148/ryai.250281 #cancer #mammo #ML

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

CTA-BET outperforms five benchmark non-commercial tools in brain mask generation on non-enhanced CT and CT angiography https://doi.org/10.1148/ryai.240847 #AI #ML #MachineLearning

#Mirai model uses lesion features on #mammograms for lesion detection and #breast cancer risk prediction. https://doi.org/10.1148/ryai.240417#Mirai#mammograms#breast#cancer #DeepLearning #explainableAI

Radiologist and AI Concordance in Screening Mammography and Association with Future Breast Cancer Risk https://doi.org/10.1148/ryai.240804 #cancer #DeepLearning #ML

A natural language processing model extracts normal kidney length and volume percentiles for pediatric patients from a large US dataset https://doi.org/10.1148/ryai.250056 #kidney #USRad #ML

Rethinking Privacy in Medical Imaging AI: From Metadata and Pixel-level Identification Risks to Federated Learning and Synthetic Data Challenges https://doi.org/10.1148/ryai.250273 #MachineLearning #deID #ML