Research & Publications

Rigorous methodology. Published benchmarks. Open validation. We document our work because clinical AI demands transparency.

Featured Research

Our published methodologies, benchmark studies, and validation frameworks.

Methodology

Hybrid Pipeline Achieves 94% TSTR Fidelity

Combining structural generation with GAN correction and LLM enrichment produces synthetic data indistinguishable from real clinical records in downstream ML tasks.

Validation Framework

6-Layer Automated Validation for Synthetic Clinical Data

A comprehensive quality framework spanning statistical fidelity, clinical pathway accuracy, temporal consistency, TSTR utility, NLP coherence, and differential privacy guarantees.

Benchmark Study

Why Raw Synthetic Data Fails Clinical AI

Commodity synthetic generators score 65-75% on Train-Synthetic-Test-Real benchmarks. We quantify the gap across clinical domains and demonstrate how hybrid correction closes it.

White Paper

On-Premise Clinical AI Without Data Exposure

Architecture and methodology for training custom hospital AI on de-identified data while maintaining full data sovereignty and HIPAA compliance.

Our Pipeline Methodology

Every dataset we produce follows a four-stage hybrid pipeline designed to close the fidelity gap that commodity generators leave open.

Stage 1

Structural Generation

Clinically-modeled patient trajectories

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Stage 2

GAN / Diffusion Correction

Trained on real data for realistic distributions

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Stage 3

LLM Enrichment

Clinical notes with hallucination detection

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Stage 4

6-Layer Validation

Statistical, clinical, temporal, TSTR, NLP, privacy

Stage 4 validation ships with every dataset as a structured report. Full methodology detail on our Synthetic Data service page.

Validation Standards

Every dataset and model we produce ships with a validation report documenting TSTR scores, distribution fidelity, clinical pathway accuracy, temporal consistency, NLP quality metrics, and privacy guarantees.

TSTR Score

Train-Synthetic-Test-Real benchmark against held-out real data

Distribution Fidelity

Statistical similarity across all features and marginals

Clinical Pathway Accuracy

Adherence to evidence-based care sequences and protocols

Temporal Consistency

Logical ordering of events across the patient timeline

NLP Quality Metrics

Coherence, specificity, and hallucination rate in clinical notes

Privacy Guarantees

Differential privacy bounds and re-identification risk scores

Upcoming Research

Active areas of investigation in our pipeline for 2026.

—Multi-modal clinical data synthesis
—Longitudinal patient trajectory modeling
—Specialty-specific model evaluation frameworks
—Privacy amplification techniques for small hospital datasets
—Federated synthetic data generation across health systems

Interested in collaborating on research?

We partner with health systems, academic medical centers, and AI labs on joint research and dataset development.

Get in Touch