FDA-adjacent device language
38%
Common around energy devices and skin tightening pages.
Benchmark Intelligence / No Hall of Shame
Own the category metric without naming operators.
The correct media moat is anonymized, operator-grade intelligence. Scrutexity should publish aggregate claim-risk benchmarks that create market pressure without making the company radioactive to the operators it needs to serve.
Guaranteed or permanent outcome claims
31%
Highest concentration in injectables, body contouring, and weight-loss copy.
Evidence-incomplete before/after claims
44%
Frequently missing consent, treatment protocol, timeline, or patient variability context.
AI answer visibility gap
57%
Operators often appear in answer engines without the evidence language they would want quoted.
Demand-at-risk context
Optional
Reported only behind claim counts, with assumptions and excluded variables visible.
Q3 2026 report structure
Top unsupported claim types
Fastest-growing risky treatment categories
GLP-1, IV therapy, peptide, and hormone claim patterns
Before-after and testimonial risk patterns
AI answer visibility and citation gaps
Demand-at-risk context with methodology and excluded variables
Questions PE buyers and insurers should ask
Distribution machine
Public teaser
Publish anonymized findings and category charts to create operator urgency.
Agency edition
Give partners a client-ready version they can use in sales and retention conversations.
PE / insurer edition
Package deeper benchmark data, diligence questions, and treatment-category risk indicators.
Monitoring conversion
End every report with a private clinic snapshot and a Guardian pilot.
30-day launch path
Start with 25-50 public websites, anonymized.
Do not wait for perfect data scale. Publish the first Medical Aesthetics Claim Risk Snapshot with clear caveats, anonymized examples, and a direct AuditGPT intake path.