- A chest abnormality model scores each chest X-ray and highlights the region that drove the score, so your team can sort the worklist before anyone opens a study.
- Image retrieval in Enterprise h2oGPTe finds visually similar prior cases in your own archive and cites each one, so a reader can check a finding against precedent.
- H2O makes no clinical efficacy claim: the model changes where your radiologist starts, not who decides.
- Patient images never have to leave your boundary, across on premises, air gapped, private cloud and cloud deployments.
Get the studies that need attention read first
Your radiology team reads chest X-rays in the order they arrive. A study with a subtle finding can sit behind dozens of normal ones, and the reader who opens it may have to hunt through the archive to find a comparable case.
That hunt is where the time goes. Prior reports and images exist, but they sit in the PACS and the reporting system, not in one place you can search by what an image looks like.
How chest abnormality detection works
Step 1: Start from your labeled archive
Use chest X-rays your radiologists have already reported, with the finding labels taken from those reports.
Step 2: Train and compare image models
H2O Driverless AI builds and ranks image classification models on your data.
Step 3: Explain each score
Region highlighting shows which part of the image drove the score, so a reader can check it.
Step 4: Sort the worklist
Your policy decides how scores reorder the queue. Nothing is closed or reported without a radiologist.
Step 5: Retrieve similar cases
Enterprise h2oGPTe runs multimodal search over your image and report collection and cites every matched case.
Governance for patient images
H2O supports HIPAA and HITECH requirements, and protected health information never has to leave your boundary. Run the model in shadow mode beside your current reading order first, and compare its output before anyone acts on it.