Clinical AI has only seen part of the world.
NextEntropy builds clinical workflow environments inside health systems in emerging markets. Models are tested and trained on real care, and the data never leaves.
Health systems keep their data.
Models come to the environment.
Revenue is shared.
Most clinical AI is measured on exam questions and Western records.
Care in Lagos, Riyadh, Bengaluru or São Paulo runs on different patients, different constraints and different systems.
Very little of it is in the data models learn from. In many countries it cannot legally be exported. So models are deployed into health systems they have never been tested against.
A working copy of a clinical workflow, with a grade for every attempt.
- Multi-step tasksReal clinical work from start to finish, not single questions.
- Real system stateThe records, orders and messages a clinician would work with.
- Consistent gradingEvery attempt is scored the same way, and every score has a reason.
- Private task setsHeld-out tasks that have never been published.
- Patient
- “My head has been paining me since yesterday and my legs are swelling. I am eight months.”
- Expected
- headache: yes · swelling: yes · third trimester
→ danger signs, escalate today - Model
- headache: yes · swelling: not captured
→ routine follow-up - Grade
- Fail. A missed danger sign changed the outcome.
An illustrative task, not a real patient record.
One environment serves the people who build models and the people who hold the data.
Test and train on clinical work you cannot reach today.
Workflows from health systems outside the US and Europe, run in place, with grading you can trust.
Earn from your workflows without giving up your data.
We build inside your systems. You decide who uses the environment, and you share in the revenue.
Maternal triage
Patient intake and risk assessment during pregnancy, graded against clinical guidelines.