Clinical AI Review Chain

Clinical AI Review Chain

Status: DEVELOPMENT REFERENCE — NOT PRODUCTION

This case describes an architecture for clinical AI feedback and validation.

Operational problem

A clinical AI workflow may produce an analysis that is later reviewed, confirmed, corrected, or rejected by a clinician.

If the original model result is overwritten, evidence about what the system actually produced is lost.

DigiEmu pattern

The reference chain is:

Clinical Artifact → Analysis Run → Immutable AI Result → Doctor Feedback Event → Validated or Corrected Result

Each review event should link to enough context to identify:

• case or artifact;
• analysis run;
• model and version;
• original AI result;
• reviewer;
• timestamp;
• prior feedback event where applicable.

Current evidence boundary

This page describes a development architecture.

It does not claim:

• production clinical deployment;
• clinical validation;
• diagnostic performance;
• medical-device approval;
• completed external verification.

Reference success criteria

A development implementation should preserve:

1. the exact original AI output;
2. the relationship between output and source artifact;
3. append-only review or correction events;
4. provenance from validated result back to the original run;
5. explicit separation between model output and human judgment.

Related material:

• AI Output to Evidence
• Artifact & Evidence Contract
• Evidence Levels