Last updated: April 24, 2026
Evaluation methodology
AI for Medical evaluates AI categories and tools with a practice-first lens: patient safety, privacy, regulatory fit, workflow reality, and evidence before feature claims.
Scoring dimensions
| Dimension | What it means | Why it matters |
|---|---|---|
| Clinical risk | How much the output can affect diagnosis, treatment, triage, or patient harm. | Higher-risk workflows require stronger evidence and review. |
| Privacy and security | PHI handling, retention, BAA, access controls, logging, and incident response. | Medical AI often touches sensitive patient data. |
| Evidence quality | Validation setting, patient population, outcome measures, and source transparency. | Benchmarks do not automatically translate into local clinical value. |
| Regulatory fit | Whether the tool is a medical device, has FDA records, or makes claims that need review. | Intended use determines the relevance of regulatory status. |
| Workflow fit | Where output appears, who reviews it, how it integrates, and how mistakes are corrected. | Even accurate tools can fail if they do not fit clinical operations. |
Editorial rule
We do not present AI as a replacement for clinicians. We do not give patient-specific medical advice. We separate lower-risk administrative AI from higher-risk clinical decision support and regulated device workflows.