How to Evaluate Wound Care AI Evidence: A Buyer's Checklist

Most wound care AI vendors lead with demos. Fewer can hand you a PubMed link. This guide walks documentation leaders and medical directors through a repeatable process to evaluate the evidence base behind any wound care AI platform before signing a contract.

Step 1 — Ask for peer-reviewed publications

Request direct citations on PubMed or an indexed journal. A whitepaper hosted on the vendor's own site is not peer review. Confirm authorship includes clinicians, not only engineers.

Step 2 — Verify the methods, not just the conclusions

Read the methods section. Look for:

• Sample size and patient population
• How ground truth was established (clinician labels, ruler measurements, biopsy)
• Reported precision, recall, or measurement error
• Whether the model was validated on data outside its training set

Step 3 — Check for responsible AI documentation

Ask how the platform handles patient education content, hallucination risk, and clinician oversight. The AI for wound care overview is one example of a vendor publishing its responsible-AI posture openly.

Step 4 — Confirm HIPAA architecture in writing

A SOC 2 report and a signed BAA are table stakes. Ask where PHI is stored, whether it is used for model training, and how de-identification is handled.

Step 5 — Map evidence to your own workflow

Research on a controlled dataset is not the same as performance at the bedside. Run a structured pilot — for example, the approach outlined in how to run a HIPAA-compliant pilot in 30 days — and measure documentation time, coding accuracy, and clinician satisfaction against your current baseline.

If a vendor cannot complete this checklist, the AI is a marketing layer, not a clinical tool.