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Speech biomarkers offer a scalable, non-invasive, cost-effective solution for automated screening. However, the clinical utility of machine learning (ML) remains limited by interpretability and generalisability to real-world speech datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We evaluate explainable ML for screening of Alzheimer\u2019s disease and related dementias (ADRD) and severity prediction using benchmark DementiaBank speech (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009291, 64% female, 69.8\u2009\u00b1\u20098.6 years). We validate generalisability on pilot data collected in-residence (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200922, 59% female, 76.2\u2009\u00b1\u20098.0 years). To enhance clinical utility, we stratify risk for actionable triage and assess linguistic feature importance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We show that a Random Forest trained on linguistic features for ADRD detection achieves a mean sensitivity of 69.4% (95% confidence interval (CI)\u2009=\u200966.4\u201372.5) and specificity of 83.3% (78.0\u201388.7). On pilot data, this model yields a mean sensitivity of 70.0% (58.0\u201382.0) and specificity of 52.5% (39.3\u201365.7). For prediction of Mini-Mental State Examination (MMSE) scores, a Random Forest Regressor achieves a mean absolute MMSE error of 3.7 (3.7\u20133.8), with comparable performance of 3.3 (3.1\u20133.5) on pilot data. Risk stratification improves specificity by 13% on the test set, offering a pathway for clinical triage. Linguistic features associated with ADRD include increased use of pronouns and adverbs, greater disfluency, reduced analytical thinking, lower lexical diversity, and fewer words that reflect a psychological state of completion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our predictive modelling shows promise for integration with conversational technology at home to monitor cognitive health and triage higher-risk individuals, enabling early screening and intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s43856-025-01263-1","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T16:46:25Z","timestamp":1765557985000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evaluating spoken language as a biomarker for automated screening of cognitive impairment"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6032-3042","authenticated-orcid":false,"given":"Maria R.","family":"Lima","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3884-8045","authenticated-orcid":false,"given":"Alexander","family":"Capstick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1112-786X","authenticated-orcid":false,"given":"Fatemeh","family":"Geranmayeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramin","family":"Nilforooshan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maja","family":"Matari\u0107","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9625-4544","authenticated-orcid":false,"given":"Ravi","family":"Vaidyanathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8591-9638","authenticated-orcid":false,"given":"Payam","family":"Barnaghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"1263_CR1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/d41573-022-00211-1","volume":"22","author":"A Mullard","year":"2023","unstructured":"Mullard, A. 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