Assuring Transparency of AI Software after Deployment https://doi.org/10.1148/ryai.260811 #AOCMP2026 #medphys #RadPhys #radiology

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

DeepVEST: Deep Learning-based Vessel Segmentation and Erasure in Breast MRI for Improved Lesion Assessment 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

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment https://doi.org/10.1148/ryai.250731 #AOCMP2026 #medphys #RadPhys #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 #AOCMP2026 #medphys #RadPhys #radiology

Assuring Transparency of AI Software after Deployment https://doi.org/10.1148/ryai.260811 #AOCMP2026 #medphys #RadPhys #radiology

MultiModalNet: a deep learning model that combines 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

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

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 #AOCMP2026 #medphys #RadPhys #radiology

Assuring Transparency of AI Software after Deployment https://doi.org/10.1148/ryai.260811 #AOCMP2026 #medphys #RadPhys #radiology

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

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