Medical AI with sealed PHI.
Confidential compute lets care teams use AI assistants, imaging models, and research workflows while protected health information stays sealed and every clinical run can carry evidence.
sealed PHI
01
PHI cannot leave the boundary
Clinical notes, medical images, lab results, and patient histories are useful to AI only if the provider and host cannot read them.
02
HIPAA needs evidence
Healthcare buyers need more than a vendor policy. They need proof of runtime isolation, no-log behavior, and audit-ready access paths.
03
Hospitals need collaboration
Research networks need shared model outputs without pooling raw PHI into a new central blast radius.
solution mockup
A doctor can ask AI without turning the AI provider into a PHI holder.
First, the clinical chat runs through private inference with a no-log build. Then the research workflow moves compute to each hospital dataset instead of moving PHI to a central operator.
Clinical AI
HIPAA · no-log CVM
private inference
No-log by construction for clinical prompts.
verified
PHI sealed
verified
TEE runtime
verified
signed receipt
Clinical Compute-to-Data
PHI stays at source · clinical AI travels · every key release is gated by attestation
Sealing at Source
Each owner runs a local sealing CLI: HKDF(kms_root_pubkey, analysis_app_id, analysis_compose_hash, owner_id). Encrypts the dataset, publishes ciphertext. Owners never ship plaintext or keys. Change the recipe → key no longer matches.
use cases from the report
Where healthcare needs confidential compute.
Doctor-facing clinical assistant
Let clinicians query notes, imaging summaries, and care context while prompts and responses stay inside a no-log clinical runtime.
Radiology AI in GPU TEE
Run imaging analysis on protected GPU capacity with attestable model execution and sealed PHI handling.
Multi-hospital cohort analysis
Analyze shared cohorts across hospitals without each owner exposing raw records to a central operator.
report
Medical AI with Sealed PHI
Founder welcome video
16 sec
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