Validation & governance
I treat validation and governance as product capability—how AI earns clinical trust, scales responsibly, and proves measurable impact in real settings.
What I focus on
- Real-world validation: end-to-end experience evaluation in clinical settings, using real patient data with clinician review.
- Consortium-led evidence: aligning stakeholders on evaluation design, adoption, and evidence expectations.
- Guardrails: policy, monitoring, escalation paths, and human-in-the-loop.
- Impact KPIs: burden reduction, consistency, throughput, decision quality, and ROI.
- Configurability with standards: enabling global variation without compromising safety and accountability.
Signals
Clinically grounded
Evidence and adoption designed for real practice.
Responsible & scalable
Lifecycle governance: monitor, learn, improve, repeat.
Measurable impact
KPIs that reflect clinical value, not vanity metrics.
Notes
Short notes on evidence, accountability, and scaling trustworthy AI.