Prepare studies
De-identify DICOM and reports; pin the dataset; hold out by patient, not by image.
Industry blueprint
Medical imaging
Radiology and pathology. De-identified studies, held out by patient, compared per finding, approved by a radiologist.
Imaging models must be trained on de-identified studies, evaluated with patients held out rather than images, and compared per finding class on sensitivity and specificity before a radiologist signs off. This design makes each of those a gated step with a record, which is what a regulated deployment later has to show.
Illustrative pipeline
Train or fine-tune
Run the partner's imaging model on your GPUs inside the budget.
Compare per finding
Sensitivity and specificity per finding class against the current model on held-out studies.
Radiologist approves
Deployment only after clinical review of the comparison. The approval is recorded.
What the record keeps
The de-identified dataset version, the patient-level split, the candidate, per-finding metrics, the spend, and the clinical approver.
Partner brings
- De-identified imaging data and reads
- Imaging model
- PACS and regulatory ownership
Cairn brings
- Patient-level held-out evaluation
- Per-finding comparison with approval
- GPU budget
- A record of every deployed model
Business value to test
Sensitivity and specificity per finding, reader time, and time to cover a new finding class.
Keep the decision explicit
Nothing is deployed without a recorded clinical approval.
Close the feedback loop
Radiologist corrections become labels for the next candidate.
Reference design, not a shipped domain pack. Healthcare deployments carry regulatory obligations (HIPAA and equivalents) that the partner must own; Cairn's approval and record trail supports them but does not satisfy them on its own.
See how failure can trigger a reviewed improvement cycle →Bring a reader study.
Talk about your modalities, de-identification and who signs off clinically.