1
Candidate patient worlds
Structured EHR, trajectories, hidden truth, and optional text sidecars are generated from the same synthetic clinical state.
Certificate-gated synthetic clinical scenarios
Find the clinical AI failures your held-out test set misses.
Synset generates clinically plausible, longitudinal, scenario-controlled synthetic cohorts with hidden truth, release certificates, and audit evidence for testing healthcare AI systems before deployment.
Cohort-level synthetic futures, not patient-specific predictions.
Validated acute-care trajectories across 96 sequential hourly transitions.
Structured EHR, trajectories, notes, dialogue, claims-style artifacts, and model-under-test reports.
Platform
Candidate worlds. Measured gates. Released evidence.
Synset does not release every generated sample. Candidate patient worlds are created first, filtered through explicit realism checks, then packaged as scenario packs, benchmark packs, audit packs, and model-hardening cohorts.
2
Unsupported futures stop here
Candidates are checked for clinical, temporal, population-level, metadata, and text-grounding constraints.
Certified outputs carry evidence
Released cohorts include evidence of the checks they passed before becoming scenario packs, benchmark packs, audit packs, or model-hardening cohorts.
Release sequence
From candidate scenario to evidence pack.
Scroll through how Synset turns plausible-looking synthetic clinical candidates into release-gated scenario infrastructure for model-under-test evaluation.
Scene 01
1Held-out test sets miss future failure modes.
A clinical AI system can pass retrospective tests and still break when context is missing, documentation shifts, or a patient state evolves.
Scene 02
2Synset generates candidate clinical scenarios.
Each candidate can include structured EHR, trajectories, hidden truth, and optional note, dialogue, or claims-style sidecars.
Scene 03
3Unsupported futures are rejected.
Candidates pass through clinical, temporal, population, metadata, and grounding checks before release.
Scene 04
4Released scenarios carry audit evidence.
A Synset certificate records which checks each output passed before it becomes a scenario pack or model-under-test report.
Scene 05
596 sequential hourly transitions.
Synset has validated acute-care trajectories across four days of hourly clinical evolution under trajectory-level certification criteria.
Scene 06
6One certified scenario pack. Multiple tests.
Released cohorts branch into structured EHR, longitudinal trajectories, grounded text, claims-style artifacts, and scenario audit packs.
Candidate scenarios to evidence packs
One active idea at a time: candidates, gates, certificates, evidence, outputs.
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 trajectory
Hidden truth
Text sidecars
clinical admissibility
transition consistency
population typicality
metadata safety
text grounding
Rejected
Outside observed clinical pattern.
Candidate accepted
Ready for release evidence.
Release certificate
SYN-SCN-0421
clinical admissibilityPASS
transition consistencyPASS
population typicalityPASS
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
Trajectory
Notes
Dialogue
Claims
Scenario audit pack
Same released synthetic state, packaged for different evaluation workflows.
Scene 01
1Held-out test sets miss future failure modes.
A clinical AI system can pass retrospective tests and still break when context is missing, documentation shifts, or a patient state evolves.
plausible record 1
Synthetic note
plausible record 2
Vitals row
plausible record 3
Claim line
plausible record 4
Risk score
Scene 02
2Synset generates candidate clinical scenarios.
Each candidate can include structured EHR, trajectories, hidden truth, and optional note, dialogue, or claims-style sidecars.
SYN-SCN-0421
Candidate clinical scenario
Structured EHR
96h trajectory
Hidden truth
Text sidecars
Scene 03
3Unsupported futures are rejected.
Candidates pass through clinical, temporal, population, metadata, and grounding checks before release.
clinical admissibility
transition consistency
population typicality
metadata safety
text grounding
Rejected
Outside observed clinical pattern.
Candidate accepted
Ready for release evidence.
Scene 04
4Released scenarios carry audit evidence.
A Synset certificate records which checks each output passed before it becomes a scenario pack or model-under-test report.
Release certificate
SYN-SCN-0421
clinical admissibilityPASS
transition consistencyPASS
population typicalityPASS
metadata safetyPASS
text groundingPASS
Scene 05
596 sequential hourly transitions.
Synset has validated acute-care trajectories across four days of hourly clinical evolution under trajectory-level certification criteria.
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
6One certified scenario pack. Multiple tests.
Released cohorts branch into structured EHR, longitudinal trajectories, grounded text, claims-style artifacts, and scenario audit packs.
EHR tables
Trajectory
Notes
Dialogue
Claims
Scenario audit pack
Same released synthetic state, packaged for different evaluation workflows.
Plausible-looking record
A synthetic record can read well and still fail measured realism gates.
Patient State T+48Rejected
MAP 49
HR 142
Lactate 0.8
SpO2 99
Transition consistency:failed
Unsupported text mention:flagged
Realism gates
The release layer decides what survives.
Synset measures realism before release.
Clinical admissibility
Values and transitions satisfy allowable constraints.
Transition consistency
Patient-state changes remain plausible across time.
Population typicality
Patterns remain consistent with cohort-level structure.
Text grounding
Generated text cannot alter structured evidence.
Market contrast
Generic synthetic data is not enough for clinical AI assurance.
The valuable question is not whether records look real. It is whether scenario-controlled cohorts can expose model failures, preserve hidden truth, and carry evidence for why they were released.
Synset turns synthetic clinical data into certifiable model-under-test infrastructure.
Typical Synthetic Data Generators
- •Generate plausible-looking records
- •Often validate rows independently
- •Provide limited evidence of longitudinal consistency
- •Treat realism as a modeling assumption
- •May leave users to discover implausible samples downstream
- •Usually separate structured data from clinical text
- •Rarely provide trajectory-level release evidence
Synset
- Generates certificate-gated synthetic cohorts
- Builds longitudinal clinical futures across time
- Creates controlled counterfactual variants
- Preserves hidden truth and answer keys
- Grounds note, dialogue, and claims sidecars to structured evidence
- Rejects unsupported futures
- Releases evidence reports and failure-mode artifacts
EvidenceView full evidence summary →
96 sequential hourly transitions.
Synset has validated acute-care trajectories across four days of hourly clinical evolution under trajectory-level certification criteria.
Timeline
- Day 0 - Admission anchor
- 24h - Review labs/vitals
- 48h - Clinical drift
- 72h - Response pattern
- 96h - Released horizon
96
Sequential hourly transitions
Validated acute-care horizon.
97
State points
Initial anchor plus 96 transitions.
0
Accepted audit violations
Out of 14,143 accepted acute candidates.
~0.021%
Upper bound
Conservative accepted-only audit.
Caveat: Accepted-only validator audit, not held-out future patient accuracy, clinical truth, regulatory approval, or synthetic-control-arm validity.
Outputs
Certified cohorts packaged for evaluation.
From the same structured synthetic evidence, Synset can package EHR tables, trajectories, notes, dialogue, claims-style artifacts, scenario audit packs, and model-under-test reports.
EHR tables
Structured records for research, benchmarking, and model evaluation.
Clinical text sidecars
Evidence-grounded notes, summaries, and documentation variants.
Longitudinal trajectory
Longitudinal patient state sequences with transition evidence.
Dialogue examples
Patient-clinician exchanges grounded to the structured trajectory.
Scenario audit pack
Controlled perturbation packs for coding, summarization, abstraction, risk, and agent workflows.
Sample packs
Certified cohorts packaged for evidence review, scenario testing, and pilot collaborations.
Request the evidence, not just the dataset →Sample output inspection
One synthetic world. Three grounded artifacts.
Generate a scenario variant, inspect hidden structured evidence, then switch between note, dialogue, and claims-style artifacts generated from the same released synthetic state.
1,450Reference points
96Hourly transitions
3Output types
SYN-CDG-3009
A1c 8.1eGFR 54SBP 146Rx metformin
Synthetic outpatient summary: chronic cardiometabolic disease with incomplete follow-up and configurable documentation density.
Sidecar 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.
Required disclaimer: Demo output for product illustration only. Structured EHR evidence controls what the note, dialogue, and claims examples can say. This is not clinical advice or real patient documentation.
Use Cases
Start where certificate-gated synthetic cohorts matter most.
Synset is most valuable where teams need controlled clinical scenarios, hidden truth, longitudinal progression, missing-context traps, documentation variants, subgroup structure, and release evidence, not just more fake rows.
Find the clinical AI failures your held-out test set misses.
Certificate-gated scenario packs can test whether model outputs remain stable across wording changes, missingness, longitudinal progression, care setting, documentation style, subgroup context, and synthetic patient dialogue.
Assistant vendors, clinical RAG vendors, health-system AI governance teams.
Clinical AI Assistant Audit
Certified synthetic case packs with hidden truth, red flags, contraindications, missing-context traps, longitudinal deterioration, and counterfactual variants.
Audit an assistant →Ambient scribe, summarization, care-navigation, coding/CDI vendors.
Note-Grounding Audit
Synthetic mini-charts with structured ground truth plus note and dialogue sidecars to test unsupported assertions, omitted facts, contradictions, medication mismatches, lab mismatches, and temporal-order errors.
Test note grounding →Regulated healthcare ML vendors, risk-model companies, payers, health systems.
Risk Model Stress-Test Pack
Scenario cohorts for calibration, false negatives, subgroup performance, drift, missingness robustness, and risk-coverage behavior.
Stress-test risk models →Vendors shipping frequent model updates and governance teams.
Model Update Regression Suite
Fixed certified audit suites rerun after model updates to detect regression, drift, degraded calibration, hallucination modes, and worsened subgroup behavior.
Build a regression suite →Core Workflow
A controlled path from synthetic evidence to model-under-test reporting.
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Synthetic patient world
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Controlled perturbations
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Model-under-test
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Scenario audit
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Failure-mode report
synset-scenario-runner
More productized wedges
Coding, CDI, and Claims Audit
Synthetic charts with known diagnoses, procedures, labs, meds, notes, and claims-style artifacts to test missed codes, unsupported codes, upcoding risk, and evidence inconsistency.
Audit coding workflows →
FHIR / EHR Sandbox Blueprint
Clinically coherent, longitudinal, scenario-controlled, certificate-bearing FHIR-style test populations for complex workflows.
Build a sandbox →
Release certificate
Every released output carries its checks.
Synset separates generation from certification. Candidate samples are generated first; only outputs that pass release checks are packaged for use.
Candidate trajectory ARejected
Fails transition consistency.
Candidate trajectory BReleased
Passes clinical plausibility gates.
Internal release record
Release Certificate
- Clinical admissibilityPASS
- Statistical typicalityPASS
- Trajectory consistencyPASS
- Metadata safetyPASS
- Text groundingPASS
Caveat: Certification means the output passed Synset's stated release checks. It does not mean the output is a real patient record, a guaranteed future, or a clinical recommendation.
Text sidecars cannot rewrite the evidence.
Synset generates optional notes, summaries, dialogues, and message-style artifacts from structured synthetic evidence. Text sidecars are rejected if they introduce unsupported facts.
local LLM generation
text certificates
numeric grounding checks
redaction audits
no cloud LLM required by default
no patient reconstruction
Imaging, genetic, and waveform data are the next frontier.
Synset is extending the same release-gate approach beyond structured EHR, trajectories, and clinical text into imaging, genetic, and waveform modalities.
- •Synthetic imaging sidecars with grounded acquisition context
- •Genetic and biomarker profiles constrained by cohort-level structure
- •Waveform segments for physiologic model stress tests
These are roadmap directions, not current validated claims.
Differentiation
A release layer for synthetic clinical data.
Synset combines synthetic EHR, longitudinal trajectories, grounded text, and audit-ready output packaging behind explicit release checks.
| Capability | Generic synthetic data | Rule-based synthetic patients | Academic EHR models | Synset |
|---|---|---|---|---|
| Synthetic EHR records | ✓ | ✓ | ✓ | ✓ |
| Longitudinal patient data | ~ limited / not release-certified | ✓ rule-based histories | ✓ sequence generation | ✓ release-gated trajectories with evidence |
| Fixed 96-hour trajectory horizon | - | - | ~ | ✓ |
| Release gates before output | ~ | ~ | ~ | ✓ |
| Trajectory-level release evidence | - | - | ~ | ✓ |
| Grounded clinical text sidecars | ~ | ~ | ~ | ✓ |
| Designed for model stress tests | ~ | ~ | ~ | ✓ |
Footnote: Category comparison based on typical public positioning. Some systems may support adjacent capabilities. A checkmark for longitudinal data means the category can represent records over time; it does not mean the same release-gated trajectory evidence, certification, or audit packaging that Synset provides. Synset's differentiation is the combination of fixed-horizon trajectories, release-gated certification, grounded text, and audit-ready output packaging.
Pilot routes
Package the scenario evidence your workflow needs.
Data evaluation teams
Inspect certified synthetic EHR, trajectory, note, dialogue, and claims-style sample packs.
Request sample pack →Healthcare AI teams
Stress-test coding, summarization, abstraction, risk, and agent workflows before deployment.
Discuss model-under-test evaluation →Clinical data partners
Explore privacy-preserving collaboration paths for synthetic cohort generation and evidence reporting.
Discuss data partnership →FAQ
The claims Synset is making, and the ones it is not.
Clear answers on measured realism, certification, validation boundaries, and cohort-level synthetic futures.
No. Synset's primary wedge is certificate-gated synthetic clinical scenario infrastructure for testing healthcare AI systems. The data matters because it carries hidden truth, controlled perturbations, longitudinal structure, and release evidence.
Still have questions? Synset is opening pilot collaborations with clinical AI teams, researchers, and healthcare organizations.
Contact
Request a clinical AI audit pilot.
Request a scenario pack, evidence materials, or a model-under-test audit discussion with the Synset team.
Direct email
synsetai@gmail.comFor audit pilots, diligence materials, scenario packs, evidence discussions, or model-under-test scoping.
01
Clinical AI Assistant Audit
For clinical RAG, triage, care-navigation, or assistant workflows.
02
Note-Grounding Audit
For summarization, handoff, scribe, and documentation workflows.
03
Risk Model Stress-Test Pack
For calibration, missingness, subgroup, drift, and risk coverage.
04
Scenario / Sample Pack
For EHR, trajectory, note, dialogue, claims, and certificate artifacts.
Useful context to include
- •model-under-test or workflow category
- •clinical domain or scenario family
- •artifact type: EHR, trajectory, note, dialogue, claims, FHIR, or audit report
- •known failure concerns: missingness, contraindications, hallucination, subgroup drift, calibration, coding errors
- •validation or diligence questions
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 generation from certification and reports validation with explicit caveats.