Key takeaways
- 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.