Clinical LLM training

Train an LLM on hospital data without exporting PHI

The practical path is to avoid exporting raw hospital data. A clinical LLM job should be packaged, constrained, and executed inside a controlled hospital or enterprise environment. The researcher receives policy-approved artifacts and proof metadata, not raw patient records.

Workflow

Define the LLM adaptation or evaluation job.
Hash and verify the model/container artifact.
Route the workload to an institution-controlled node.
Enforce output policy, logging, and egress limits.
Return only approved model artifacts, metrics, hashes, and receipts.

Where Rapha Protocol participates

Rapha Protocol provides a compute-to-data coordination layer: secure API authentication, ZK-TLS/session boundaries, enterprise-node execution posture, and Polygon mainnet proof anchors. It is designed to make clinical AI work inspectable without making raw PHI portable.

Important limitations

LLM training can memorize data if output controls are weak. Production deployments need privacy testing, leakage evaluation, model-card documentation, access logs, security review, and legal agreements before real patient data is involved.

Do not use public Rapha Protocol demos for real hospital data or PHI. Public flows are private-alpha demonstrations only.