For Clinicians & Health Systems · Lead magnet
Clinical AI Red Flags: A Clinician's Evaluation Framework
What to look for before an AI tool enters a clinical workflow. Written for clinicians, practices, health systems, and executives. Patients are welcome to read it — the decisions it describes belong to the people who adopt the tool.
Dr. Adarsh K. Gupta, DO, MS · Clinical AI Strategist · adarshgupta.com
What it covers
Seven red flags
Aviation treats preventable error as unacceptable and keeps a human in the cockpit. This report asks whether a clinical AI tool meets a comparable bar — then gives you the question to put to the vendor. The full essay and scoring checklist arrive by email.
1. Regulatory status
Is the tool FDA-cleared for a specific clinical indication, or is that status buried?
2. Explainability
Can you trace a recommendation back to the data that produced it?
3. Training data
Does the model’s population match the patients you actually see?
4. Physician in the loop
Does a clinician review the output before any patient-facing action?
5. Failure modes
Can the vendor name how the model fails, not only how it scores?
6. Real-world outcomes
Is there evidence from routine use, or only from controlled studies?
7. Adverse event reporting
Is there a closed loop when the tool contributes to a miss or a delay?
Get the report
Email required — then the full report
Educational strategy resource — not legal, regulatory, or clinical advice for a specific deployment. Pair with institutional policy and, for evidence grading, CertaintyGrade.com.