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Updated: Aug 5, 2026
Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
Published on: March 1, 2015
Artificial intelligence tools for the assessment and management of dysphagia: a scoping review
Elayidath Vasudevan Sreedevi1, Subramania Iyer K2, Krishnakumar Thankappan1
1Department of Head and Neck Surgery and Oncology, Amrita Vishwa Vidyapeetham, Amrita School of Medicine, Kochi, India.
Objectives:
To explore the existing artificial intelligence (AI) tools used in the assessment and management of dysphagia.
Design:
Scoping 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.
Methods:
The 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.
Results:
A 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.
Conclusion:
The 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.
