Frequently asked questions.

Synset finds measured weaknesses in supported clinical AI models and determines which may be correctable.

Where justified, Synset creates targeted training material and tests an updated model on held-out real data.

Readiness assessments, bounded model diagnostics, and synthetic scenario or QA packages are available now.

Model-hardening pilots are open to selected design partners. Regression and local validation are expansion paths.

Model hardening is a bounded attempt to correct a specific, measured weakness and test whether the change holds up on held-out real data.

No. Some weaknesses need better labels, additional real evidence, a different model or workflow, or a narrower intended use.

A defensible decision not to proceed is a useful result.

A defined model task and intended use, a reproducible inference path, a clear target, and available development data or model outputs.

A hardening pilot also requires a customer-controlled held-out real evaluation set and prespecified success and no-regression criteria.

The customer may train the updated model using a Synset cohort and frozen protocol.

Where agreed, Synset may support a bounded training workflow. Universal autonomous retraining is not available today.

Scenario-based text systems can be assessed when responses can be imported or reached through an approved OpenAI-compatible endpoint. This can include clinical assistants, RAG workflows, documentation models, and coding/CDI models.

Structured prediction models are scoped case by case. Hardening requires a reproducible training or adaptation path, a defined target or output contract, and held-out real evaluation data. API-only models without an adaptation path may be assessed but not hardened. Imaging and unsupported modalities remain partner-driven.

Synset compares frozen original and updated models on held-out real data using prespecified success and no-regression criteria.

The final holdout is not used to select, tune, or choose the intervention.

Synset prepares a better-tested model and technical evidence for review. It does not determine submission suitability, approval, or deployment authorization.

For supported workflows, evaluation can run inside the customer's controlled environment so raw patient data does not need to leave.

Customer-ready local deployment remains an enterprise design-partner path.

Current evidence supports Synset's clinical-world generation and checking infrastructure and an implemented bounded diagnostic workflow.

Closed-loop model-hardening proof on held-out real data remains the next decisive milestone.