Guides, project breakdowns, and lessons learned from building medical imaging platforms — written by the PYCAD team.
RADAR is a new multimodal benchmark testing whether AI can review and improve draft radiology reports against 3D imaging studies — and why it matters for medical imaging product teams.
A session-based radiology reporting workflow: read naturally in the viewer, narrate findings, and turn the review into a structured draft report.
Merlin shows how CT-native vision-language foundation models could shape radiology AI, with released code, model weights, and dataset access for researchers.
PadChest-GR brings grounded, bilingual evaluation to chest X-ray report generation. Learn why this benchmark matters for trustworthy radiology AI and medical imaging workflows.
ABRA introduces a more realistic way to evaluate radiology AI by testing how models function inside real DICOM workflows with OHIF, Orthanc, and structured reporting tools.
A controlled experiment comparing three approches on the same synthetic clinical note to see how OpenMed models perform compared to an LLM.
We build custom medical imaging platforms — advanced DICOM viewers, AI segmentation, and the clinical systems around them.
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