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Early identification improves outcomes, yet many patients\u2014especially in low-resource settings\u2014lack access to gold-standard evaluations. This scoping review explores the integration of artificial intelligence (AI) and data science\u2014defined as the interdisciplinary use of computational methods, statistical modeling, and machine learning to extract clinically meaningful patterns from biomedical data\u2014in PSD screening and assessment. We synthesize evidence from bedside screening instruments, acoustic voice analyses, and emerging AI-driven models for dysphagia and aspiration risk stratification, critically appraising limitations related to small datasets, overfitting risk, and the need for external validation. Traditional tools like the water swallow test show high sensitivity but varying specificity; recent studies support augmenting these with voice-based biomarkers such as post-swallow wet voice, jitter, and shimmer. While wet voice as a standalone marker has limited sensitivity (8\u201329%), its high specificity (75\u201394%) within multimodal approaches justifies continued investigation. AI models trained on acoustic parameters have demonstrated strong performance in detecting penetration-aspiration events, while mobile and voice-based platforms may expand diagnostic reach, pending further validation. We also review optimal screening timing, emphasizing assessment within 24 h of stroke onset with repeated evaluations for high-risk patients. Future directions advocate multimodal, patient-centered approaches combining wearable biosensors, cloud-based analytics, and culturally adapted algorithms, while addressing implementation challenges including infrastructure requirements, digital literacy, workflow integration, and ethical considerations. The convergence of clinical expertise and computational technologies presents a promising path to equitable, scalable, and precise dysphagia care.<\/jats:p>","DOI":"10.1007\/s10439-026-04267-7","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T17:59:59Z","timestamp":1782928799000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Screening and Evaluation of Post-stroke Dysphagia: Insights from Neurology, Artificial Intelligence and Data Science\u2014A Scoping Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-7164","authenticated-orcid":false,"given":"Joao Brainer Clares","family":"de Andrade","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah Negreiros","family":"de Carvalho Leite","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandra Braga Cruz Guedes","family":"de Morais","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thiago S","family":"Carneiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priscila Correia","family":"Fernandes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimas Irion","family":"Alves","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"issue":"1","key":"4267_CR1","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1186\/s12877-022-02960-5","volume":"22","author":"KJ Banda","year":"2022","unstructured":"Banda, K. 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