{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:19:30Z","timestamp":1792790370862,"version":"4.5.0"},"posted":{"date-parts":[[2026,9,28]]},"group-title":"Rehabilitation Medicine and Physical Therapy","reference-count":45,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"National Institute on Deafness and Other Communication Disorders","award":["3R01DC020965-02S1"],"award-info":[{"award-number":["3R01DC020965-02S1"]}],"id":[{"id":"https:\/\/ror.org\/04mhx6838","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Institute on Deafness and Other Communication Disorders","award":["R01DC020965"],"award-info":[{"award-number":["R01DC020965"]}],"id":[{"id":"https:\/\/ror.org\/04mhx6838","id-type":"ROR","asserted-by":"crossref"}]},{"name":"President and Fellows of Harvard College"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Discourse analysis can reliably predict real-world communicative success, yet is rarely implemented clinically, due to challenges in manually generating stimulus-specific main concept inventories (MCIs) and scoring patient narratives. We evaluated whether automatically derived macrolinguistic (main concept, sentiment) and microlinguistic (fluency, lexical, syntactic) features extracted from movie clip narrations can augment the clinical assessment of discourse in aphasia. The MCIs generated directly from each clip\u2019s video, audio and subtitles using a vision-language model approximated the main concepts healthy controls produced. Group classification (control: n=51; aphasia: n=54) based on the discourse features was excellent (AUC=0.95, 95% CI [0.90, 0.99]), with macrolinguistic features showing the largest group differences (main concept completeness: d=-1.84, 95% CI [\u22122.30, \u22121.38]) and strongest associations with aphasia severity (semantic distance to inventory: r=-0.79, 95% CI [\u22120.87, \u22120.66]). Findings support the feasibility and scalability of automated criterion-based discourse assessment using ecological stimuli, and highlight its potential for clinical implementation.<\/jats:p>","DOI":"10.64898\/2026.09.27.26364108","type":"posted-content","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:45:15Z","timestamp":1790635515000},"source":"Crossref","is-referenced-by-count":0,"title":["Assessing spoken discourse in aphasia using multimodal artificial intelligence"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1837-601X","authenticated-orcid":false,"given":"M. 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for Technology in Psychiatry, McLean Hospital"},{"name":"Department of Psychological and Brain Sciences, University of Massachusetts Amherst"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6040-6254","authenticated-orcid":false,"given":"M.","family":"Varkanitsa","sequence":"additional","affiliation":[{"name":"Center for Brain Recovery, Boston University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.M.","family":"Girard","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of Kansas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Brito","sequence":"additional","affiliation":[{"name":"Speech and Hearing Bioscience and Technology, Harvard 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