For Clinicians & Health Systems · Lead magnet
Clinical AI Safety & Evaluation Guide
A strategist guide for evaluating clinical AI before procurement or go-live — problem framing, evidence claims, safety governance, model behavior, and adoption habits. Enter your work email to receive the full guide. It is not published openly without signup.
Dr. Adarsh K. Gupta, DO, MS · Clinical AI Strategist · adarshgupta.com
What's inside
1. Problem & Context
Define the clinical problem, intended users, care setting, and failure modes that could harm patients.
2. Evidence & Claims
Map vendor claims to measurable outcomes, study design, external validation, and evidence certainty.
3. Safety & Governance
Human oversight, audit trails, bias and equity for your population, and compliance review paths.
4. Model Behavior
Hallucination risk, staged rollout, change-control when models update, and written stop criteria.
5. Adoption & Habits
Training burden, workflow friction, champion clinicians, and success metrics beyond clicks.
Get the guide
Email required — then the full guide
Educational strategy resource — not legal, regulatory, or clinical advice for a specific deployment. Pair with institutional policy and, for evidence grading, CertaintyGrade.com.