Setting Up a Governance Framework for AI in Clinical Genomics

A governance framework for AI in clinical genomics is a mandatory control system, not an optional best practice. It transforms AI from a research tool into a regulated clinical device by enforcing audit trails for every model decision, establishing a versioned model registry, and defining clear Human-in-the-Loop (HITL) workflows for high-risk predictions. This technical scaffolding is essential for compliance with CLIA/CAP and builds institutional trust in automated diagnostics.

Implementation requires specific tools and protocols. You will set up a model registry using MLflow to track experiments, lineage, and deployments. For auditability, you must log all inputs, model versions, outputs, and reviewer actions to an immutable database. Crucially, you will engineer approval gates—such as requiring geneticist sign-off for variants of uncertain significance—directly into the inference pipeline, creating a transparent, defensible process for every patient case.