Question 1

Does this replace clinical judgment?

Accepted Answer

No. Zyogen automates structure, consistency, and parameter suggestion. Your clinical team leads interpretation and the final graft decision.

Question 2

Is this generative AI inventing grafts?

Accepted Answer

No. The stack is deterministic. A physics engine computes bioink formulation, scaffold geometry, and print parameters from wet-lab data, not hallucinated outputs.

Question 3

How does graft readiness prediction work?

Accepted Answer

A vision model maps wound-bed composition, granulation, fibrin, necrosis, and related conditions. Those fractions feed predictors for whether the bed can take a skin graft and how healing is likely to run.

Question 4

Why do you need interpretability?

Accepted Answer

Medical models can secretly key on rulers, skin tone, or lighting. We audit internals so the system is trusted for the right biological reasons before it informs a decision.

Question 5

Where does patient data live?

Accepted Answer

Sovereign deployment: self-hosted and on-prem inside hospital infrastructure, designed for DPDP and HIPAA compliance.

Question 6

Can this connect to existing bioprinters?

Accepted Answer

Yes. A hardware translation layer produces toolpaths and extrusion protocols for multi-modal bioprinters already installed in research and clinical settings.

Biology first. Then software that hospitals can trust.

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