Clinical AI Robustness Testing
Find where clinical AI models break before customers do.
Synset generates clinically plausible synthetic perturbations to stress-test coding models, summarizers, risk models, triage systems, and clinical agents.
Core Workflow
Use Case: ICD Coding
Generate clinically plausible note and trajectory variants to test whether a coding model remains stable across:
- alternative wording
- missing information
- longitudinal progression
- care-setting transitions
- subgroup context
- documentation style
- synthetic patient dialogue
Deliverables
coding stability report
sensitivity maps
failure clusters
overconfidence analysis
adversarial examples
robustness scorecard
Use Case: Clinical Summarization
Test whether summarizers preserve clinical facts, avoid hallucinations, respect missingness, and remain stable across note styles.
Use Case: Risk Prediction
Generate plausible trajectories and evaluate whether prediction models are sensitive to missingness, drift, or subgroup context.
Generate the world. Perturb the evidence. Audit the model.
Synset turns synthetic clinical generation into model-under-test infrastructure.