Predetermined Change Control Plan Adoption and Documentation Transparency in U.S. Food and Drug Administration–cleared Radiology Artificial Intelligence/Machine Learning Devices https://doi.org/10.1148/ryai.260385 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

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 #ML #MachineLearning #Radiomics

Human-AI Collaboration in Radiology: The Blind Spots https://pubs.rsna.org/doi/10.1148/ryai.260325 shk03.bsky.social #ASFNR26 #NeuroRad #fMRI #MRI #radiology

AI improves image quality, but there are important limitations of postprocessing algorithms https://doi.org/10.1148/ryai.260100 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): Explanation, Elaboration, and Examples https://doi.org/10.1148/ryai.260835 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Deep Learning Analysis of Dual-Modality US Videos for the Characterization of Superficial Lymphadenopathy https://doi.org/10.1148/ryai.260131 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

After the 2024 FDA guidance, adoption of Predetermined Change Control Plans (PCCPs) increased among radiology AI devices, but better reporting transparency is needed. https://doi.org/10.1148/ryai.260385 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Human-AI Collaboration in Radiology: The Blind Spots https://pubs.rsna.org/doi/10.1148/ryai.260325 shk03.bsky.social #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Image Quality in the Era of Artificial Intelligence: Understanding the Limitations of AI-based Image Reconstruction and Postprocessing https://doi.org/10.1148/ryai.260100 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): Explanation, Elaboration, and Examples https://doi.org/10.1148/ryai.260835 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Deep Learning Analysis of Dual-Modality US Videos for the Characterization of Superficial Lymphadenopathy https://doi.org/10.1148/ryai.260131 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Predetermined Change Control Plan Adoption and Documentation Transparency in U.S. Food and Drug Administration–cleared Radiology Artificial Intelligence/Machine Learning Devices https://doi.org/10.1148/ryai.260385 #ASFNR26 #NeuroRad #fMRI #MRI #radiology

Check out today's #FreeFriday @radiology-ai.bsky.social article from #PubMedCentral!

Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Using a Longitudinally Aware Segmentation Network

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127956 #ML #DeepLearning #Radiomics

New Special Report! Three underexplored domains drive the success of human-AI collaboration in radiology https://pubs.rsna.org/doi/10.1148/ryai.260325 shk03.bsky.social #ASFNR26 #NeuroRad #fMRI #MRI #radiology

AI improves image quality, but there are important limitations of postprocessing algorithms https://doi.org/10.1148/ryai.260100 #ASER2026 #EMRad #radiology

Understanding the 2024 CLAIM update with examples and clarifications https://doi.org/10.1148/ryai.260835 #ASER2026 #EMRad #radiology

Deep Learning Analysis of Dual-Modality US Videos for the Characterization of Superficial Lymphadenopathy https://doi.org/10.1148/ryai.260131 #ASER2026 #EMRad #radiology

After the 2024 FDA guidance, adoption of Predetermined Change Control Plans (PCCPs) increased among radiology AI devices, but better reporting transparency is needed. https://doi.org/10.1148/ryai.260385 #ASER2026 #EMRad #radiology

#ThrowbackThursday – #AutoQC provides a robust framework to automatically curate chest radiographs (May 2025) https://doi.org/10.1148/ryai.250135 #AI #ML #MachineLearning

Human-AI Collaboration in Radiology: The Blind Spots https://pubs.rsna.org/doi/10.1148/ryai.260325 shk03.bsky.social #ASER2026 #EMRad #radiology