Phase 1: Cardiovascular Prototype (MVP)

Gretel.ai is a leading synthetic data platform selected for its ability to balance privacy, utility, and regulatory compliance in healthcare applications. Here’s why it stands out:

HIPAA Compliance and Privacy Safeguards

Gretel generates synthetic data that mimics real patient records without exposing sensitive PHI (Protected Health Information). Its anonymization techniques, such as differential privacy and data masking, ensure compliance with regulations like HIPAA and GDPR.

Unlike traditional anonymization, which risks re-identification, Gretel’s synthetic data cannot be reverse engineered to trace back to individuals.

High Fidelity to Real-World Data

Gretel’s models (e.g., ACTGAN, Amplify) replicate statistical patterns, correlations, and distributions of real datasets. For example, synthetic patient age distributions in clinical trials closely mirror original data, preserving critical trends like disease prevalence across demographics.

In benchmark tests, Gretel’s synthetic data achieved <2% accuracy loss compared to real data in downstream tasks like heart disease prediction.

Scalability for Complex Use Cases

Gretel supports relational databases, enabling synthetic versions of multi-table EHR systems (e.g., patient records linked to lab results and treatments). This ensures referential integrity and realistic data relationships.
It scales to generate large datasets (e.g., 10k+ synthetic patient records) efficiently, addressing data scarcity in rare diseases or underrepresented populations.

Bias Mitigation
By augmenting imbalanced datasets (e.g., boosting female patient records in male-dominated heart disease data), Gretel reduces algorithmic bias. One case saw a 13% accuracy gain in models trained on synthetic-augmented data.