Training AI on Wilson disease? You need 100s of patients. But Wilson disease only affects 1 in 30,000 people. Even large hospitals have 5-10 cases.
HIPAA, GDPR, patient consent, IRB approvals... The legal complexity of sharing real patient data makes research nearly impossible.
Collecting real patient data costs $5-10M and takes 3-5 years. Most startups can't afford this timeline.
Generate unlimited rare disease patients instantly, with zero privacy risk, at a fraction of the cost
Our synthetic patients aren't random noise. They're generated using the TTT Framework, trained on peer-reviewed medical literature and expert-validated disease progressions.
Not "anonymized" real data. These patients never existed. Differential privacy guarantees (ε=1.0) make re-identification mathematically impossible.
Every cohort has a seed value. Generate "Fabry Cohort, Seed 42" and anyone can recreate the exact same patients. Critical for research reproducibility.
Export as FHIR R4 bundles - the global standard for healthcare data. Load directly into EMRs, databases, or AI training pipelines.
Generate 500 synthetic Pompe patients to predict treatment response, design enrollment criteria, and estimate market size. Save $8M and 3 years vs. real data collection.
Typical Client: Biotech developing enzyme replacement therapy
Train rare disease diagnostic AI on 10,000 synthetic patients across 50 diseases. Achieve 88% real-world accuracy despite zero real training data.
Typical Client: Healthcare AI startup building diagnostic tools
Study genetic mutations and their clinical effects using 300 synthetic Fabry patients with 15-year timelines. Publish reproducible findings without privacy concerns.
Typical Client: University rare disease research team
Submit 1000-patient synthetic validation dataset to FDA as supplementary evidence for AI diagnostic algorithm approval. Reproducible, privacy-safe, clearly labeled.
Typical Client: Medical device company seeking FDA clearance
25-100 synthetic patients with complete 10+ year medical histories, longitudinal labs, symptom timelines, genetic variants, and treatment responses. Delivered as JSON + FHIR R4 bundles.
Scientific document proving clinical realism: DCR score (92% typical), false positive rates, confounder analysis, and comparison to peer-reviewed literature.
Markdown documentation explaining generation methods, privacy guarantees (differential privacy epsilon), limitations, licensing (Research Use Only), and reproducibility (seed values).
Our team will contact you within 2 business days
Questions? Email info@liet-research.eu