Large-scale fine-tuning produced a highly accurate large language model for automated radiology impression generation in a multicenter evaluation https://doi.org/10.1148/ryai.250714 #LargeLanguageModel #radiology #MachineLearning

The LumbarDISC dataset: multisequence, multiplanar adult MRI lumbar spine data from 8 institutions, with image level annotations https://doi.org/10.1148/ryai.250480 @utah.edu #DataResources #MedicalImaging #AI

AIRFRAME part 1: developing a deep-learning algorithm for identifying posterior fossa abnormalities on first-trimester US screen scans https://doi.org/10.1148/ryai.250394 @fprefumo.bsky.social #ultrasound #RadUS #ML

A Note of Thanks to our wonderful reviewers for their time and expertise! https://doi.org/10.1148/ryai.260176 #AI #reviewers #appreciation

Interpretable Machine Learning Model Using Digitized US Features for Classifying Complex Thyroid Nodules https://doi.org/10.1148/ryai.250383 #AACR26 #AACR2026 #cancer #OncoRad #oncology

A Taxonomy of Machine Hallucination in Radiology https://doi.org/10.1148/ryai.250203 #AIhallucination #ML #MachineLearning

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

Large-scale fine-tuning produced a highly accurate large language model for automated radiology impression generation in a multicenter evaluation https://doi.org/10.1148/ryai.250714 #AI #ML #MachineLearning

Using prompts that mimic common cognitive biases reduces the accuracy of several LLMs on radiology board-style questions https://doi.org/10.1148/ryai.250585 #LLM #ML #MachineLearning

Editor’s Recognition Awards honor our excellent reviewers https://doi.org/10.1148/ryai.260178 #AI #ML #MachineLearning

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

Dr. Ahmed Maiter discusses the importance of direct comparisons between radiology AI tools in new blog post https://radiologyai.substack.com/p/taking-devices-head-to-head-a-critical #ArtificialIntelligence #AI #MachineLearning

A new AI tool using US video to diagnose breast cancer offers similar performance to foundation models with fewer parameters and can be deployed on portable US devices https://doi.org/10.1148/ryai.250600 #AACR26 #AACR2026 #cancer #OncoRad #oncology

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

A Note of Thanks to our wonderful reviewers for their time and expertise! https://doi.org/10.1148/ryai.260176 #AI #appreciation #ML

An interpretable AI approach to accurately classify complex thyroid nodules with digitized ultrasound features https://doi.org/10.1148/ryai.250383 #AACR26 #AACR2026 #cancer #OncoRad #oncology

Development of a Deep Learning Algorithm for Posterior Fossa Abnormality Recognition on First-Trimester US Screening Scans: AIRFRAME Study Part 1 https://doi.org/10.1148/ryai.250394 @fprefumo.bsky.social #JRC2026 #radiology

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset https://doi.org/10.1148/ryai.250480 @utah.edu #JRC2026 #radiology

New from the Vasty Deep: How comparative benchmarking studies inform which AI tool might be best for your radiology practice https://radiologyai.substack.com/p/taking-devices-head-to-head-a-critical #JRC2026 #radiology

Intratumoral heterogeneity spatial-contextual modeling facilitates prediction of breast cancer response to neoadjuvant therapy https://doi.org/10.1148/ryai.250760 #SBI2026 #Mammo #BreastCancer #BreastRad

A Note of Thanks to our wonderful reviewers for their time and expertise! https://doi.org/10.1148/ryai.260176 #JRC2026 #radiology

Development of an Integrated Deep Learning Approach for Detecting Fetal Brain Abnormalities in Routine Second Trimester Ultrasound Scan: A Multicenter Study https://doi.org/10.1148/ryai.250737 #JRC2026 #radiology

Supporting model trustworthiness by understanding failure modes: a new taxonomy of AI hallucination https://doi.org/10.1148/ryai.250203 #JRC2026 #radiology

New Vasty Deep post: A call for institutions to prioritize ongoing validation of Radiology AI tools. https://radiologyai.substack.com/p/what-happens-after-the-algorithm #JRC2026 #radiology

Dr. Ahmed Maiter discusses the importance of direct comparisons between radiology AI tools in new blog post https://radiologyai.substack.com/p/taking-devices-head-to-head-a-critical #JRC2026 #radiology

UltraMC identified complex thyroid nodules and satisfactorily diagnosed conventional and mummified thyroid nodules in a large, multi-center study https://doi.org/10.1148/ryai.250383 #AACR26 #AACR2026 #cancer #OncoRad #oncology

Data Resources are datasets and related artifacts to support scientific research and clinical implementation of #AI tools https://pubs.rsna.org/page/ai/data_resources#AI#ML #MachineLearning #DeepLearning

New mammography dataset with BI-RADS, breast density, machine ID, and outcome data freely available to the research community https://doi.org/10.1148/ryai.250375 #SBI2026 #Mammo #BreastCancer #BreastRad

Routine fetal second-trimester US images and new end-to-end deep learning pipeline allow for brain anomaly detection https://doi.org/10.1148/ryai.250737 #JRC2026 #radiology

Clinic-aligned Dual Distillation of Video and Image Foundation Models for Automated Breast Cancer US Diagnosis https://doi.org/10.1148/ryai.250600 #SBI2026 #Mammo #BreastCancer #BreastRad

A Taxonomy of Machine Hallucination in Radiology https://doi.org/10.1148/ryai.250203 #JRC2026 #radiology

Taking devices head-to-head: a critical step for choosing the correct AI device for clinical trials and deployments https://radiologyai.substack.com/p/taking-devices-head-to-head-a-critical #JRC2026 #radiology

Intratumoral heterogeneity spatial-contextual modeling facilitates prediction of breast cancer response to neoadjuvant therapy https://doi.org/10.1148/ryai.250760 #SBI2026 #Mammo #BreastCancer #BreastRad

Feeling grateful to the expert reviewers who supported Radiology AI in 2025 https://doi.org/10.1148/ryai.260178 #JRC2026 #radiology

Interpretable Machine Learning Model Using Digitized US Features for Classifying Complex Thyroid Nodules https://doi.org/10.1148/ryai.250383 #AACR26 #AACR2026 #cancer #OncoRad #oncology

New Vasty Deep post: A call for institutions to prioritize ongoing validation of Radiology AI tools. https://radiologyai.substack.com/p/what-happens-after-the-algorithm #JRC2026 #radiology

USDist: a US dual-distillation model for automated breast cancer diagnosis deployed on a portable US device https://doi.org/10.1148/ryai.250600 #SBI2026 #Mammo #BreastCancer #BreastRad

Taking devices head-to-head: a critical step for choosing the correct AI device for clinical trials and deployments https://radiologyai.substack.com/p/taking-devices-head-to-head-a-critical #JRC2026 #radiology

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset https://doi.org/10.1148/ryai.250480 @utah.edu #JRC2026 #radiology

#ThrowbackThursday – MammosighTR dataset provides BI-RADS-labeled mammograms with detailed annotations on breast composition & lesion quadrant location (Oct 2025) https://doi.org/10.1148/ryai.240841#ThrowbackThursday#ML #MachineLearning #Radiomics

Routine fetal second-trimester US images and new end-to-end deep learning pipeline allow for brain anomaly detection https://doi.org/10.1148/ryai.250737 #JRC2026 #radiology

2025 Manuscript Reviewers: A Note of Thanks https://doi.org/10.1148/ryai.260176 #JRC2026 #radiology

Recognizing our most engaged and thorough reviewers of 2025 with the Editor's Recognition Awards https://doi.org/10.1148/ryai.260178 #ARRS26 #ARRS2026 #radiology

Lisa Adams discusses what happens after algorithms go live in new Vasty Deep post https://radiologyai.substack.com/p/what-happens-after-the-algorithm #ARRS26 #ARRS2026 #radiology

Dr. Ahmed Maiter discusses the importance of direct comparisons between radiology AI tools in new blog post https://radiologyai.substack.com/p/taking-devices-head-to-head-a-critical #ARRS26 #ARRS2026 #radiology

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

USDist: a US dual-distillation model for automated breast cancer diagnosis deployed on a portable US device https://doi.org/10.1148/ryai.250600 #ARRS26 #ARRS2026 #radiology

Our reviewers are the backbone of the journal. We thank them for sharing their knowledge. https://doi.org/10.1148/ryai.260176 #ARRS26 #ARRS2026 #radiology

Experimenting with regulations: The role of regulatory sandboxes in governing AI for radiology https://radiologyai.substack.com/p/experimenting-with-regulations-the #ARRS26 #ARRS2026 #radiology

Development of an Integrated Deep Learning Approach for Detecting Fetal Brain Abnormalities in Routine Second Trimester Ultrasound Scan: A Multicenter Study https://doi.org/10.1148/ryai.250737 #ARRS26 #ARRS2026 #radiology