ArticleCoDAS2025
Artificial intelligence in the diagnosis and management of dysphagia: a scoping review.
Article in CoDAS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial intelligence tools for the assessment and management of dysphagia: a scoping review.BMJ open · 2026Article
- Review
- Artificial intelligence for surgical management of benign esophageal disease: scoping review and evidence mapping.Langenbeck's archives of surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis scoping review aimed to map and synthesize evidence on technological advancements using Artificial Intelligence in the diagnosis and management of dysphagia. We followed the PRISMA guidelines and those of the Joanna Briggs Institute, focusing on research about technological innovations in dysphagia. RESEARCH STRATEGIES: The protocol was registered on the Open Science Framework platform. The databases consulted included EMBASE, Latin American and Caribbean Health Sciences Literature (LILACS), Livivo, PubMed/Medline, Scopus, Cochrane Library, Web of Science, and grey literature. SELECTION CRITERIA: The acronym 'PCC' was used to consider the eligibility of studies for this review. DATA ANALYSIS: After removing duplicates, 56 articles were initially selected. A subsequent update resulted in 205 articles, of which 61 were included after applying the selection criteria.
resultsVideofluoroscopy of swallowing was used as the reference examination in most studies. Regarding the underlying diseases present in the patients who participated in the studies, there was a predominance of various neurological conditions. The algorithms used varied across the categories of Machine Learning, Deep Learning, and Computer Vision, with a predominance in the use of Deep Learning.
conclusionTechnological advancements in artificial intelligence for the diagnosis and management of dysphagia have been mapped, highlighting the predominance and applicability of Deep Learning in examinations such as videofluoroscopy. The findings suggest significant potential to improve diagnostic accuracy and clinical management effectiveness, particularly in neurological patients. Identified research gaps require further investigations to solidify the clinical applicability and impact of these technologies.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.