Tested model updates
When a fix passes evaluation, you receive the updated model and a record of what changed.
Accelerate your healthcare AI development from prototype to bedside
Stress-test and strengthen your model with clinical world simulations, building evidence for clinical validation and FDA review.
Find weak points before clinical validation.
We examine your model, data, and test results for uneven accuracy, overconfident predictions, and under-tested patient groups. Those findings guide which clinical scenarios to stress-test next.
The evidence gap
A promising model can still have blind spots that familiar checks miss.
Teams need to know which findings are supported, which remain uncertain, and whether the evidence justifies a change before the next validation study.
- Missing or underrepresented clinical scenarios
- Unstable behavior across patient contexts or evidence conditions
- Insufficient technical evidence for external review
- Model updates that reintroduce previously observed weaknesses
What you get from Synset.
We test targeted fixes to your healthcare AI model and measure the results. You get before-and-after performance comparisons, reusable stress tests, and priorities for clinical validation.
Before-and-after performance report
Compare performance before and after each tested fix, see remaining weaknesses, and prioritize clinical validation.
Stress tests you can rerun
Reuse the same clinical scenarios after future model updates to check whether earlier weaknesses return.
Current evidenceView the full evidence summary →
Checked so far: 96-hour acute-care trajectories.
Synset has internally checked acute-care trajectory generation across 96 sequential hourly transitions.
Scenario checks and model evaluation support different claims; proof follows the question and available evidence.
96
Sequential hourly transitions
Maximum support-checked acute-care horizon.
Technical note
Methodology and provenance
Review the candidate funnel, accepted-only recheck, artifact hash, and claim boundary.
The public result is a support-checking result. It is not future patient accuracy, clinical truth, regulatory readiness, synthetic-control-arm validity, or proof that a model improved.
Investigation example
Inspect a consequential question from evidence through adjudication.
These illustrative scenes show what a bounded investigation may examine. A real finding requires valid failure adjudication, a permitted intervention where justified, and independent proof matched to the claim; a negative or uncertain result can lead to no change or a stop.
Scene 01
1Profile the evidence.
Set the intended use, inspect available evidence, and mark what remains unknown.
Scene 02
2Set a controlled challenge.
Test a declared question with the evidence and controls appropriate to its scope.
Scene 03
3Adjudicate the finding.
Separate observed behavior, evidence limitations, and reviewer interpretation.
Scene 04
4Decide whether to intervene.
Attempt a permitted, relevant intervention only when the finding justifies it.
Scene 05
5Separate checks from proof.
Choose sealed synthetic, grouped cross-fitting, or stronger real-world evidence to match the claim.
Scene 06
6Preserve the complete result.
Record positive, negative, and uncertain findings for future regression review.
Profile, adjudicate, decide, and preserve
Define the question. Review the evidence. Decide whether to act. Match proof to the claim.
plausible record 1
Synthetic note
plausible record 2
Vitals row
plausible record 3
Claim line
plausible record 4
Risk score
Scene 01
1Profile the evidence.
Set the intended use, inspect available evidence, and mark what remains unknown.
plausible record 1
Synthetic note
plausible record 2
Vitals row
plausible record 3
Claim line
plausible record 4
Risk score
Illustrative review questions
Inspect example questions for a hardening study.
These preset illustrations pose questions about documentation and time-dependent evidence. They contain no evaluated model responses, coding decisions, or pricing; results would require a declared protocol and adjudication.
Illustrative example · review questions only
Illustrative challenge question
Would a coding suggestion change when follow-up documentation is sparse but relevant facts stay fixed?
Persona
Illustrative outpatient documentation question; no model result asserted
Scenario
Illustrative coding question
Timeline
Question about documentation completeness
Content
Prompts, review questions, and status notes
Illustrative
Not evaluated
No result claimed
SYN-CDG-3009
Hypothetical recordFollow-up detail variesNo coding result
Illustrative challenge prompt: review an outpatient record with incomplete follow-up and identify which details remain uncertain.
Question: what additional documentation, if any, would be needed before proposing a code? No code or reimbursement result is supplied.
This is a question for review, not an evaluated model output or coding recommendation.
Illustrative prompt:
Which facts in this hypothetical record are documented, and which remain unknown?
Reviewer question:
Would a coding suggestion be justified from the available documentation?
Review question:
What additional evidence would be required, and when should the system abstain?
Status:
Illustrative question only · not evaluated
Pricing status:
Tariff not supplied. No coding, support, or reimbursement determination is shown.
Illustrative review only · not evaluated. Tariff not supplied. No code, support decision, or reimbursement amount is shown.
What this shows: Preset illustrative questions only. Selecting “Next illustration” cycles these examples; it does not generate or evaluate model output. No clinical advice, coding result, pricing, or evidence of improvement is provided.
Ways to engage
Start at the stage your model needs.
Model Hardening leads. Deep Auditing is the diagnostic entry and can stand alone when findings and next steps are the useful outcome.
Lead offering
Model Hardening
Investigate a consequential weakness, attempt an allowed intervention where justified, and evaluate under the declared proof mode. Results may show improvement, no effect, remaining uncertainty, or a reason to stop.
Discuss your model →Diagnostic entry
Deep Auditing
Assess available support and investigate behavior, returning findings and next steps without implying intervention or improvement.
Discuss your model →FAQ
Frequently asked questions.
Synset is building the clinical AI hardening layer: map the evidence, challenge consequential gaps, test defensible interventions, and preserve what the results establish.
Model Hardening leads; Deep Auditing is the diagnostic entry. The waitlist records prospective interest and does not guarantee a pilot or launch date.
Company contact
Talk with Synset about your model.
Share high-level context about the model, its intended use, and the decision ahead. A specific failure does not need to be known in advance.
Discuss your modelUseful context to include
- •model type and declared clinical use
- •clinical domain and target population
- •the decision ahead, even if the specific failure is not yet known
- •what evidence and evaluation paths may be available
- •pilot timeline and decision to support
Safety note: Synset does not request direct PHI in website inquiries. Keep pilot notes high-level until a proper collaboration and data-handling path is established.
Evidence note: Review the evidence summary before your pilot discussion. Synset separates synthetic-world checks from model-improvement evidence.