Last updated: May 20, 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.
Product directory rule
Profiles in the product directory are written as verification starting points, not endorsements. Each profile should identify the intended workflow, audience, risk level, limits, source-backed summary, pricing signal, verification checks, and official source links.