Regulatory Readiness with Synthetic Data — Part 3: A Playbook for AI Developers
Part 3 of a 3-part series on regulatory readiness with synthetic data.
TL;DR — Pair real-world validation with targeted synthetic cohorts. Log every generation parameter, audit subgroup performance, prove non-memorization, and ship a single “evidence bundle” that a reviewer can rerun.
Principles regulators reward
- Transparency: Every artifact is reproducible from logged seeds, prompts/parameters, and versions.
- Coverage: You can show why cohorts were chosen and how they close gaps in real data.
- Robustness: Stress tests and subgroup audits are first-class, not an appendix.
- Comparability: Synthetic results are always paired with real external validation.
Provenance & traceability
Create a minimal “data card” for every synthetic batch and store it in Git or your registry. At minimum:
Generation
- Engine/version & commit
- Seed range & sampler
- Physics/protocol params (e.g., vendor, field strength, dose)
- Pathology recipe & prevalence
Outputs
- Count by class, modality, anatomy
- Masks/labels schema version
- QC results (fidelity score, clinician pass rate)
Governance
- Access controls & hashes
- Non-memorization report ID
- Reviewer rerun script path
Coverage matrix & cohort design
Define a simple matrix that maps Patient × Acquisition × Disease. Mark real-data empties, then design synthetic cohorts to fill them.
| Axis | Examples | Real gaps | Synthetic cohort target |
|---|---|---|---|
| Patient | Pediatrics, pregnancy, BMI>35 | Pediatrics, very high BMI | 2k pediatrics; 1.5k BMI 35–45 |
| Acquisition | Vendor A/B, 1.5T/3T, low-dose CT | Low-dose CT, Vendor B 3T | 3k low-dose CT; 1k Vendor B 3T |
| Disease | Stage I–IV, multifocal | Early stage, subtle lesions | 1k Stage I subtle; 800 multifocal |
Bias audits & stress tests
Ship a fixed battery of tests so results are comparable across releases:
- Subgroup metrics: AUROC/F1 by age band, sex, site, vendor, BMI.
- Protocol shift: Train on Vendor A, test on Vendor B (synthetic + real).
- Edge conditions: Motion, low dose, borderline lesions.
- MRMC (if reader study): Reader variability vs model, with synthetic hard cases.
Reproducibility & non-memorization checks
- Near-dup search: Perceptual hash + deep features against all source real datasets. Report nearest-neighbor distances & thumbnails.
- Leak tests: Train on synthetic-only; verify no suspicious spikes on real test for specific IDs.
- Determinism: Rerun with same seeds to prove identical outputs; bump engine version to show controlled drift.
Documentation: model card & data card
Bundle concise documents reviewers can skim quickly:
Model Card (4–6 pages)
- Intended use & contraindications
- Training datasets (real + synthetic) with counts
- Evaluation plan & success thresholds
- Subgroup/bias results & mitigations
- Monitoring & update policy
Data Card (per cohort)
- Provenance & parameters (see above)
- Coverage matrix deltas
- QC/clinical review notes
- Non-memorization evidence
Submission bundle checklist
| Item | Contents | Where |
|---|---|---|
| Executive memo | Intended use, risk profile, summary of evidence | /docs/memo.pdf |
| Model card | Training data, methods, results, monitoring | /docs/model-card.pdf |
| Data cards | Real + synthetic cohorts with provenance | /docs/data-cards/*.pdf |
| Audit pack | Subgroup tables, stress tests, MRMC (if any) | /evidence/audits/* |
| Non-memorization | NN search, hash stats, leak tests | /evidence/privacy/* |
| Repro scripts | One-click rerun with pinned seeds | /repro/run.sh |
| External validation | Real-world site(s) results, CIs | /evidence/external/* |
| Change log | Version diff, dataset deltas | /CHANGELOG.md |
Suggested timeline (6–8 weeks)
- Week 1: Lock coverage matrix, draft evaluation plan, freeze protocol settings.
- Week 2–3: Generate cohorts + QC; run first subgroup/stress battery.
- Week 4: Iterate cohorts to close remaining gaps; finalize non-memorization report.
- Week 5: External real-world validation; compute CIs & delta vs baseline.
- Week 6: Package bundle (model/data cards, audits, repro scripts); internal review.
- Week 7–8 (buffer): Address reviewer questions; prepare addenda.
FAQ
How much synthetic data is “too much”?
There’s no single ratio. Use synthetic to close documented gaps, then prove generalization on real external datasets. Reviewers care about the evidence chain, not a quota.
Do we need clinician review of synthetic images?
Yes, at least a spot-check with predefined criteria (artifact rate, anatomical plausibility, lesion realism). Log pass/fail and show examples.
What if results regress when we add synthetic?
Down-weight or isolate problematic cohorts; use your coverage matrix to identify the cause (e.g., protocol mismatch). Document the decision trail in your change log.