Compute-to-data

Compute-to-data for clinical AI

Compute-to-data is the pattern where compute moves to the data instead of data moving to the compute. For clinical AI, this means model jobs run inside controlled hospital, imaging, or device environments while raw patient records remain inside the data holder boundary.

Why clinical AI needs this pattern

Real clinical data is high value because it reflects live care delivery: EHR records, radiology workflows, lab signals, telemetry, and patient-generated health data. Centralizing those records in an AI company's cloud creates serious privacy, security, contractual, and regulatory risk.

Rapha Protocol uses compute-to-data as the default posture: model code, job intent, and proof sessions move toward the controlled environment; raw PHI does not move outward to public infrastructure.

How Rapha Protocol applies it

A researcher declares a model job and permitted output policy.
The secure API handles authentication and proof/session setup.
An enterprise node executes the workload beside the local clinical data.
Only policy-approved outputs, hashes, model artifacts, and proof metadata leave the boundary.
Polygon mainnet can anchor public proof receipts for auditability.

What to verify

A production compute-to-data deployment should verify container digests, network egress policy, output constraints, identity and consent rules, audit logs, key custody, incident response, and legal agreements.

Rapha Protocol is private-alpha infrastructure. Public demos must not receive real PHI or regulated production data. A compute-to-data architecture is not by itself a legal compliance certification.