For hospitals and clinics
Models trained on
clinical outcomes.
Catch disease earlier, route urgent patients to the right specialty, prescribe more safely and keep chronic care on track.
Capabilities
What the models do.
Screening
Diabetic retinopathy on fundus photographs. Tuberculosis and nodules on chest X-rays.
Endoscopy
Polyp and early gastric lesion detection during the procedure. Follow-up tracking for incidental findings.
ECG
Atrial fibrillation, conduction disease and reduced ejection fraction from a 12-lead.
Chronic disease
Titration in diabetes and hypertension. Surveillance in hepatitis B and chronic kidney disease.
Triage and referral
Referral urgency and specialty routing from the record at presentation.
Medication safety
Renal dosing, interaction and polypharmacy checks at prescribing.
Deployment
The data never has to leave.
- Wherever the data has to stay
- On-premise, inside your own cloud tenancy, or quantised at the clinic edge for sites with intermittent connectivity.
- Supervision by default
- Every output is attributable, reviewable and overridable. Autonomy is earned per task, per site, in public.
- Monitoring you share
- Drift, calibration and override rates on a dashboard owned jointly with your clinical governance lead, with agreed rollback triggers.
How a pilot runs
A clear evaluation, then a decision.
- I
Choose the pathway
Define one clinical problem and one metric the department already uses.
- II
Agree the test
Set the comparator, success bar and stopping rules in writing before the model runs.
- III
Run in shadow mode
Run the model beside the existing pathway without changing care, then compare its output with current practice.
- IV
Make the decision
Review the result together. If the bar is met, decide whether to proceed to deployment.