A six-stage diagnostic pipeline combining specialist vision encoders with large-scale clinical reasoning — producing radiologist-grade reports from any imaging modality in under three seconds.
The Problem
Existing models output a probability score per disease class. A radiologist still has to interpret the output, map it to patient context, write the report, and determine next steps.
Fine-tuned classifiers trained on single modalities break when deployed on different scanners, patient demographics, or imaging protocols.
We separate visual understanding from clinical reasoning into two distinct layers — a specialist encoder for pixel-level pathology detection, and a reasoning layer that synthesizes findings into actionable clinical reports.
The bridge between them is an auditable JSON schema — meaning every clinical conclusion has a traceable, verifiable evidence chain.
The Pipeline
The reasoning layer never sees raw pixels. It reasons over structured, verified findings from the encoder — dramatically reducing hallucination risk while enabling a full audit trail for regulatory compliance and FDA SaMD pathways.
Benchmark Results
Coverage
Technical Approach
Our open-weight reasoning layer is fine-tuned with QLoRA on curated clinical chain-of-thought datasets — producing interpretable, step-by-step diagnostic logic that mirrors how senior radiologists actually reason.
Unlike black-box classifiers, every conclusion is traceable back to a specific encoder finding, with a structured JSON audit trail that satisfies FDA SaMD and EU MDR documentation requirements.
The fine-tuned model runs on two A100s for inference — or via API for zero-infrastructure deployments. Latency under 2 seconds for the reasoning layer alone.
Early Access
We're onboarding a limited cohort of radiology departments, hospital systems, and clinical AI teams. Upload your own images. Run the full pipeline. See the report.
Demo access · De-identified images only · No PHI stored