Integrating AI Models into Radiology Workflows

The promise of AI in radiology is real — well-trained models can match or exceed radiologist performance on specific tasks. But most deployments fail not because the models are bad, but because the integration is.

Passive Observation First

PrizMed's AI pipeline works by passively observing the imaging data stream rather than inserting itself into the critical path. When a new study flows through /api/v2/imaging/stream, we fork a copy to the inference pipeline in parallel.

Handling Uncertainty

We display a calibrated probability distribution and highlight specific image regions that contributed to the model's output. This gives radiologists the context they need for clinical judgment.