Physician-Led AI Research Lab
Clinical-Grade AI for Healthcare Organizations
We build synthetic datasets, custom clinical models, and validation pipelines with the rigor your research and operations demand. Founded by a physician. Delivered by engineers.
10B+
Record Capacity
94%
TSTR Target
6
Validation Layers
HIPAA
Compliant
What We Build
Comprehensive AI services designed for healthcare's unique requirements
Synthetic Data Generation
Clinical-grade synthetic patient records validated against real-world distributions. For research, model training, and regulatory submissions.
Custom Hospital AI
Private clinical AI trained on your de-identified data, deployed on-premise with full data sovereignty.
Foundation Models
Domain-specific AI from 7B to 400B+ parameters. Pre-trained on clinical data, fine-tuned for your use case.
Our Methodology
A rigorous multi-stage pipeline — not commodity data generation
Stage 1
Structural Generation
Clinically-modeled patient trajectories
Stage 2
GAN / Diffusion Correction
Trained on real data for realistic distributions
Stage 3
LLM Enrichment
Clinical notes with hallucination detection
Stage 4
6-Layer Validation
Statistical, clinical, temporal, TSTR, NLP, privacy
Research & Publications
Peer-reviewed methodology. Published benchmarks. Open validation.
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 12 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.
Physician-Led. Engineer-Built.
Founded by Stephen J. Ronan, MD — a board-certified plastic surgeon who bridges clinical medicine and machine learning. Our team combines medical domain expertise with production AI engineering and NVIDIA DGX infrastructure.
Tell Us About Your Project
Share your goals and we'll discuss how clinical-grade AI can serve your organization.
Tell Us About Your Project