Petabytes of patient imaging. Expensive to store, dangerous to keep.
RNDA eliminates raw data at ingest — storage costs crater and the breach surface disappears. Nothing to store means nothing to lose.
Request a Healthcare POC →The Problem
Healthcare organizations store petabytes of imaging data — X-rays, CT scans, MRIs — primarily for retrieval and comparison. Breaches cost an average of $10.9M per incident. The data must exist to be stolen. RNDA eliminates that assumption entirely.
How RNDA Solves It
Zero raw data retained
Patient images are encoded on ingestion and permanently discarded. No PHI exists in the system after encoding — it cannot be breached, subpoenaed, or leaked.
Disease-discriminating similarity search
Submit any medical image and retrieve the most clinically similar cases in milliseconds. Proven on 2,000 real NIH chest X-rays with a discrimination gap of 0.82 — same disease consistently surfaces at the top of results.
Federated network search without data sharing
Hospital networks can query across institutions without any raw patient data crossing institutional boundaries. Each facility encodes locally. Only signatures are shared. Zero PHI ever leaves the building.
HIPAA by design, not policy
When no raw patient data exists, HIPAA compliance becomes structural rather than procedural. Audit trails reference signatures, not images. The breach surface is zero.
How RNDA Applies
Storage Elimination
A 415KB chest X-ray becomes 256 bytes. At 1,618x compression, a hospital generating 50 PB/year could reduce imaging storage costs by orders of magnitude. Applicable to X-rays, CT scans, MRIs, and all DICOM data.
Privacy Protection
Patient imaging data is encoded at ingest and permanently discarded. No PHI exists in the system after encoding — HIPAA compliance is architectural, not procedural. The data cannot be breached because it no longer exists.
Compliance Management
Define which imaging data gets encoded and discarded versus retained in structured form. HIPAA, HITECH, and state privacy law compliance enforced at the pipeline level — not in a separate audit process.
Intelligent Retrieval
Submit any medical image and find the most clinically similar cases across your encoded archive. Proven on 2,000 real NIH chest X-rays — same disease surfaces first, discrimination gap 0.82, results in 35ms.
Collaborative Intelligence
Hospital networks query across institutions without raw patient data crossing boundaries. Each facility encodes locally. Only signatures are shared. Zero PHI leaves the building — federated research without federated risk.
Storage Impact
Industry stat: 50 PB/year generated by hospitals globally (World Economic Forum / HealthTech Magazine, 2024)
50,000 TB × 20% RNDA adoption × $276/TB/year ÷ 1,618x compression
Industry-scale storage elimination — 1,618x compression on real NIH imaging data
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: NIH ChestX-ray14 dataset — real labeled chest X-rays
What Becomes Possible
"A radiologist submits a chest X-ray. In 35ms, RNDA returns the most clinically similar cases from a database of 2,000 real NIH X-rays — Effusion matches Effusion, Nodule matches Nodule. The original scan is permanently gone the moment encoding completes. No PHI ever existed in the system."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
Request a Healthcare POC →