How to Evaluate Any AI Tool in 5 Minutes

You don't need a CS degree to evaluate an AI tool. You need a framework. Here's the one we use.

The 5-Point Evaluation Framework

### 1. Accuracy & Validation - What evidence supports this tool's claims? Published studies or vendor benchmarks? - Was it tested prospectively or only retrospectively? - Was it tested on populations similar to yours? - What are the known failure modes? Every tool has them. - Only 5% of FDA-cleared radiology AI devices underwent prospective testing

### 2. Integration & Workflow Fit - Does it integrate with your EHR or does it create another window? - 84% of physicians say EHR integration is required for AI adoption - Does it reduce your work or shift it? Ambient scribes shift documentation from creation to review - Where in the workflow does it sit? Pre-visit, during visit, or post-visit?

### 3. Compliance & Security - Is there a signed BAA that explicitly covers AI/ML processing? - Where is the data stored? US-only residency? - Does the vendor use your data for model training? Can you opt out? - SOC 2 Type II at minimum; HITRUST preferred

### 4. Cost & ROI - Total cost including setup, training, and QA time — not just license fees - Multiply recovered clinician minutes by fully loaded hourly cost - Build conservative, expected, and best-case scenarios - Treat burnout reduction as supporting evidence, not the core business case

### 5. Evidence of Effectiveness - Is there peer-reviewed evidence, or only vendor case studies? - Has it been tested in real clinical workflows, not just benchmarks? - The Epic Sepsis Model was no more accurate than a coin flip in real-world deployment despite vendor claims - Ask: if this tool were wrong, how would I know?