For Clinicians & Health Systems
Clinical AI Safety & Evaluation Checklist
Use this before procurement or go-live. GRADE methodology depth lives on CertaintyGrade; this hub checklist keeps the strategist questions in one place.
Dr. Adarsh K. Gupta, DO, MS · Clinical AI Strategist · adarshgupta.com
1. Problem & Context
- Clinical problem is defined in PICO (or equivalent) terms
- Intended users (physician, NP/PA, nurse, patient) are named
- Care setting and workflow insertion point are explicit
- Failure modes that could harm patients are listed
2. Evidence & Claims
- Vendor claims map to measurable outcomes (not demos alone)
- Study design, population, and endpoints are documented
- External validation (or plan for it) is available
- Certainty of evidence is graded (e.g., GRADE-style) rather than assumed
3. Safety & Governance
- Human oversight model is clear (who can override, when)
- Alert/override audit trail exists
- Bias and equity risks for your population are assessed
- IRB / compliance / privacy review path is identified
4. Model Behavior
- Hallucination / fabrication risk is acknowledged for generative features
- Shadow-mode or staged rollout plan exists before full go-live
- Change-control when the underlying model updates is defined
- Stop criteria (when to pull the tool) are written down
5. Adoption & Habits
- Training burden and cognitive load for clinicians are realistic
- Trigger–craving–action–reward friction for use is understood
- Champion clinicians and escalation path are named
- Success metrics include clinician trust and override rates, not only clicks
This checklist is educational and does not constitute legal, regulatory, or clinical advice for a specific deployment. Pair with institutional policy and, for evidence grading, CertaintyGrade.com.