ArticleBMJ open2026
Artificial intelligence tools for the assessment and management of dysphagia: a scoping review.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo explore the existing artificial intelligence (AI) tools used in the assessment and management of dysphagia.
designScoping review. DATA SOURCES: MEDLINE (Ovid), Scopus, CINAHL (EBSCOhost), Cochrane Library, Joanna Briggs Institute (JBI) Evidence Synthesis, ProQuest and Google Scholar were searched for studies published between January 2000 and May 2025. ELIGIBILITY CRITERIA: Studies involving adults diagnosed with dysphagia that applied AI techniques for screening, diagnosis, prognosis or management were included. Non-human studies, paediatric-only studies, non-clinical simulations and studies without direct clinical application were excluded.
methodsThe review was conducted in accordance with the JBI methodology and reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. Two reviewers independently screened titles and abstracts, assessed full-text articles for eligibility and extracted data using a standardised data charting form. Data were synthesised using narrative synthesis.
resultsA total of 4863 records were identified, of which 27 studies met the inclusion criteria, representing 8842 participants. Most studies (21/27, 77.8%) published between 2013 and 2025 and conducted in the USA (9/27, 33.3%), South Korea (6/27, 22.2%), Japan (3/27, 11.1%) and China (3/27, 11.1%). The majority of studies (21/27, 77.8%) employed machine learning or deep learning approaches applied to videofluoroscopic swallowing studies, fibreoptic endoscopic evaluation of swallowing, wearable sensor data, acoustic signals or structured clinical datasets. Most studies are prospective (8/27, 29.6%) and retrospective (7/27, 25.9%) investigations; case series (3/27, 11.1%). Only one randomised controlled trial (1/27, 3.7%) evaluated AI-based dysphagia intervention. AI applications primarily focused on screening and assessment tasks.
conclusionThe current literature demonstrates a growing body of AI-based tools for dysphagia assessment and screening, with comparatively limited evidence for management and rehabilitation applications. Most studies highlighting the need for large-scale and multicentre studies to support safe and equitable integration of AI into dysphagia care pathways. TRIAL REGISTRATION NUMBER: DOI 10.17605/OSF.IO/DYCE9.
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