Large language models deconstruct the clinical intuition behind diagnosing autismClinical assessment is the gold standard for autism diagnosis, and by using amply pre-trained language models paired with a suitable interpretability strategy, the native analysis of clinical intuition itself is now possible, guided by detailed observational text reports from clinicians. Our interpretable language model framework identified repetitive and stereotyped behaviors as being empirically more relevant for autism diagnosis than social signs in the reports, motivating the possible revision of existing diagnostic instruments.Clinical assessment is the gold standard for autism diagnosis, and by using amply pre-trained language models paired with a suitable interpretability strategy, the native analysis of clinical intuition itself is now possible, guided by detailed observational text reports from clinicians. Our interpretable language model framework identified repetitive and stereotyped behaviors as being empirically more relevant for autism diagnosis than social signs in the reports, motivating the possible revision of existing diagnostic instruments.Jack Stanley, Emmett Rabot, Siva Reddy, Eugene Belilovsky, Laurent Mottron, Danilo Bzdokhttps://www.cell.com/cell/fulltext/S0092-8674(25)00213-2?rss=yeshttp://www.cell.com/cell/inpress.rssCellCell RSS feed.Wireless News CampaignMarch 27, 2025

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