Last updated: April 24, 2026

Medical AI governance checklist

Use this before a demo becomes a pilot. Medical AI should enter a practice through a controlled workflow, not an enthusiastic experiment with unclear accountability.

• Intended use: Write exactly what the tool will and will not do.
• Risk class: Mark whether it touches PHI, billing, diagnosis, treatment, triage, or patient messaging.
• Evidence: Request validation evidence for the same use case, specialty, and patient population.
• Privacy: Confirm BAA availability, data retention, training use, deletion, and access controls.
• Security: Review SOC 2, ISO 27001, penetration testing, audit logging, and incident response.
• Regulatory status: Check FDA records when the tool claims device status or influences clinical decisions.
• Workflow: Define where the AI output appears, who sees it, and who can override it.
• Human review: Make all outputs draft-only unless policy explicitly permits otherwise.
• Measurement: Track time saved, error types, clinician satisfaction, patient concerns, and compliance issues.
• Stop rule: Decide in advance what defect rate, privacy issue, or workflow failure pauses the pilot.

Minimum pilot record

Keep a one-page record with the vendor, workflow, reviewer, start date, source documents, contract status, data controls, success metrics, and stop rule. This turns a software trial into a governed medical operations decision.