1
Discover
Find where the model fails and determine whether the gap can be corrected.
Discover and remediate blind spots in your clinical AI prototypes
How it works ↓Translation workflow
Discover the gap. Harden what is remediable. Prove what changed.
2
Harden
Generate targeted synthetic clinical worlds and support training of an updated model candidate.
Prove
Compare it with the original on held-out real data and retain the failures as regression tests.
Synset's clinical world model creates the targeted synthetic clinical scenarios needed to test gaps the original development set does not cover.
Why Synset
A promising model can still stall in translation.
Synset connects the steps required to move from a measured model weakness to a defensible next decision.
What stalls progress
- •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
How Synset moves the model forward
- Map the relevant model weakness
- Determine whether it can be addressed
- Generate targeted synthetic clinical worlds
- Support a bounded model or workflow intervention
- Compare the updated model on held-out real data
- Preserve the relevant cases as regression tests
What can be addressed
Match each blind spot to the right intervention.
Synset separates weaknesses that may respond to a bounded intervention from those that need new real evidence, a different model or workflow, or a narrower intended use.
1
Correctable with a bounded intervention
Additional data, calibration, thresholds, preprocessing, or abstention may address the measured weakness.
2
Needs new real evidence
The model needs better labels, better measurement, or more real clinical data before a defensible intervention.
3
Stop or narrow
The defensible result may be a different model, a changed workflow, or a narrower intended use.
Deliverables
What a bounded engagement can produce.
A useful result may be an updated model, a requirement for new real labels, a narrower intended use, or a defensible decision not to proceed.
Failure map
Where a supported model breaks across populations, contexts, and evidence conditions.
What can be addressed
Which gaps may respond to targeted data or another bounded model change.
Targeted training cohort
Checked synthetic clinical worlds built around one measured weakness.
Versioned intervention record
The updated model, targeted training data, protocol, and evaluation plan.
Held-out comparison
A scoped comparison on held-out real data using prespecified success and no-regression criteria.
Remaining risks and regression tests
What remains unsupported and which fixed tests to rerun after future updates.
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.
Certificates govern synthetic scenarios. Held-out real data tests the model change.
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.
Targeted data
From model blind spot to targeted training and evaluation data.
See how Synset turns one measured weakness into targeted training and evaluation data while keeping checks on synthetic worlds separate from proof that a model improved.
Scene 01
1Define one model weakness.
Set one intended use, one measured weakness, and one evaluation question.
Scene 02
2Generate controlled clinical worlds.
Create records, timelines, notes, and variants around that weakness.
Scene 03
3Reject worlds that break the rules.
Clinical, evidence, timing, metadata, and text checks reject unsupported scenarios.
Scene 04
4Freeze the hardening cohort.
Record the cohort version and why it passed the checks required for this use.
Scene 05
5Separate checks from proof.
Synthetic-world checks do not establish that an updated model performs better.
Scene 06
6Package it for the job.
Use checked worlds for training, evaluation, or permanent regression tests.
Measured weakness to frozen cohort
Define the gap. Generate and check the cohort. Freeze the protocol. Test the model.
plausible record 1
Synthetic note
plausible record 2
Vitals row
plausible record 3
Claim line
plausible record 4
Risk score
SYN-SCN-0421
Candidate clinical scenario
Structured EHR
96h timeline
Answer key
Notes and dialogue
clinical fit
transition consistency
population support
metadata safety
text grounding
Rejected
Outside observed clinical pattern.
Candidate accepted
Accepted for this study.
Release certificate
SYN-SCN-0421
clinical fitPASS
transition consistencyPASS
population supportPASS
metadata safetyPASS
text groundingPASS
Day 0
Admission anchor
24h
Review labs/vitals
48h
Clinical drift
72h
Response pattern
96h
Released horizon
Accepted-only validator audit, not held-out future patient accuracy.
EHR tables
Timeline
Notes
Dialogue
Claims
Scenario pack
The same checked scenarios can support a bounded intervention and future regression tests.
Scene 01
1Define one model weakness.
Set one intended use, one measured weakness, and one evaluation question.
plausible record 1
Synthetic note
plausible record 2
Vitals row
plausible record 3
Claim line
plausible record 4
Risk score
Scene 02
2Generate controlled clinical worlds.
Create records, timelines, notes, and variants around that weakness.
SYN-SCN-0421
Candidate clinical scenario
Structured EHR
96h timeline
Answer key
Notes and dialogue
Scene 03
3Reject worlds that break the rules.
Clinical, evidence, timing, metadata, and text checks reject unsupported scenarios.
clinical fit
transition consistency
population support
metadata safety
text grounding
Rejected
Outside observed clinical pattern.
Candidate accepted
Accepted for this study.
Scene 04
4Freeze the hardening cohort.
Record the cohort version and why it passed the checks required for this use.
Release certificate
SYN-SCN-0421
clinical fitPASS
transition consistencyPASS
population supportPASS
metadata safetyPASS
text groundingPASS
Scene 05
5Separate checks from proof.
Synthetic-world checks do not establish that an updated model performs better.
Day 0
Admission anchor
24h
Review labs/vitals
48h
Clinical drift
72h
Response pattern
96h
Released horizon
Accepted-only validator audit, not held-out future patient accuracy.
Scene 06
6Package it for the job.
Use checked worlds for training, evaluation, or permanent regression tests.
EHR tables
Timeline
Notes
Dialogue
Claims
Scenario pack
The same checked scenarios can support a bounded intervention and future regression tests.
Synthetic clinical worlds
Inspect the synthetic scenarios behind a hardening study.
Each checked world keeps the clinical evidence consistent across structured records, notes, dialogue, and claims-style outputs. Controlled variants probe a specific model weakness without changing the clinical facts arbitrarily.
96Hourly transitions
3Output types
Blind spot
Coding confidence changes when follow-up evidence is sparse, despite unchanged clinical state.
Persona
Outpatient chronic-care profile with configurable follow-up gaps
Scenario
Coding robustness variant
Timeline
Structured encounter evidence
Outputs
Note, dialogue, claims-style outputs
prior check
redaction pass
grounding pass
SYN-CDG-3009
A1c 8.1eGFR 54SBP 146Rx metformin
Synthetic outpatient summary: chronic cardiometabolic disease with incomplete follow-up and configurable documentation density.
Generated text preserves known evidence, avoids unstated complications, and can vary wording around adherence, medication history, and follow-up gaps.
Chronic trajectory modeling remains prototype-stage and is not presented as held-out patient-future prediction.
What this shows: Synthetic output for product illustration only. Structured evidence controls what the note, dialogue, and claims-style examples can say. This is not clinical advice, real patient documentation, billing guidance, or proof that a model improved.
Ways to engage
Start at the stage your model needs.
Translation readiness is the default entry point. Hardening follows only when a measured weakness is supported and correctable.
Available now
Translation Readiness Assessment
Identify what is preventing one model from moving toward external review and define the shortest defensible path forward.
Start a translation assessment →Selected design-partner pilot
Model Hardening Pilot
Target one correctable weakness and test a frozen intervention on held-out real data.
Discuss a hardening pilot →Recurring expansion
Regression Suite
Preserve discovered failures and rerun them after model or workflow changes.
Plan regression testing →synset-scenario-runner
Example workflow: load a failure surface, generate and check targeted variants, freeze the hardening cohort, and prepare it for held-out evaluation.
FAQ
Frequently asked questions.
Synset finds measured weaknesses in supported clinical AI models, determines which can be corrected, creates targeted training data, and tests updated models on held-out real data.
Translation Readiness Assessments are available now. Selected design partners can discuss Model Hardening Pilots.
Contact
Bring one model, one intended use, and one failure question.
Tell us what the model does, where you suspect it fails, and whether a held-out real evaluation set exists and where it can be evaluated.
Direct email
info@synset.aiFor translation readiness assessments, model hardening pilots, specialist model assessments, regression evidence, and local validation design partnerships.
01
Translation Readiness Assessment
Identify the blockers to external review and define the shortest defensible path forward.
02
Model Hardening Pilot
Target one supported, correctable weakness under a frozen protocol.
Useful context to include
- •model type and declared clinical use
- •clinical domain and target population
- •suspected weakness or evidence gap
- •whether a held-out evaluation set exists and where it can be evaluated
- •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.