AI in Radiology and Radiomics 101: Resources and Roadmap
I have a lot of colleagues who want to learn AI to improve their radiology practice. They frequently ask me how to learn radiomics, machine learning, and deep
I have a lot of colleagues who want to learn AI to improve their radiology practice. They frequently ask me how to learn radiomics, machine learning, and deep
How I built a Telegram bot in a weekend that turns voice notes into formatted ultrasound reports, using Whisper, Claude, and a library of my own templates.
A pillar reference for radiology normal values across ultrasound, CT, and MRI — adult and pediatric — with quick-reference tables and clinical caveats.
Interpreting ultrasound images requires a deep understanding of anatomical structures and their sonographic appearance. The challenge many residents, and
Determining whether imaging measurements are normal or abnormal is fundamental to radiology, yet memorizing thousands of reference values across all
Understanding bone age is crucial for pediatric healthcare who need to assess a child's skeletal development and growth patterns. A bone age
NVIDIA MAISIhttps://docs.nvidia.com/nim/medical/maisi/latest/overview.html can generate synthetic 3D CT images up to 512×512×768 voxels that are
This dataset comprises 1,937,450 DICOM images (totaling 980.24 GB) drawn from 7,279 training studies, 650 public test studies, and 1,517 private test studies
A radiologist's journey from clinical frustration to creating the Easy Bone Age Atlas app that's transforming pediatric radiology workflows worldwide.