White Paper
A Practical Framework for Medical Imaging AI Validation
Why cleared AI algorithms keep failing in the real world, and the three-tier framework that closes the gap between a regulatory approval and a tool clinicians actually trust.
What you'll find inside
Why hospitals and AI vendors keep hitting the same validation bottleneck, and the three-tier framework, from technical checks to real-world monitoring, that turns validation into a continuous process instead of a one-time hurdle.
Including a national-scale case study that validated multiple AI models against over 200,000 patients across 20 hospitals.
Download the full white paper to see exactly how it was done.
See it in action
The 5 Hidden Truths About Federated Learning in Medical AI
Why federated learning looks appealing on paper, and where it breaks down for real-world AI validation.
2025
DICOM Anonymizer: Safeguarding Patient Privacy in Medical Imaging
What gets de-identified in a DICOM file, and where header-only anonymization still leaves patients exposed.
Sep 30, 2024
Multi-Site Reader Studies in Medical Imaging & AI Research
How multi-institution reader studies compare algorithms and human readers on the exact same dataset.
Sep 2, 2024