SynthesisHead & neck2025
A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.
Synthesis in Head & neck, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Article
- Videomics and artificial intelligence in endoscopic diagnosis of laryngeal lesions: mapping current evidence through a scoping review.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Multicenter Clinical Validation of an Artificial Intelligence Diagnostic Classification Model for Laryngoscopy Images.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEndoscopy is essential in upper aerodigestive tract (UADT) examination, particularly in the early detection of laryngopharyngeal lesions. However, UADT endoscopy remains operator-dependent and lacks standardized quality metrics. Recent advancements in artificial intelligence (AI) have generated interest in applications within UADT endoscopy. This review evaluates the clinical impact of AI in UADT endoscopy.
methodsA literature review was conducted up to December 31, 2024. Studies were evaluated using the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool.
resultsEighty-three studies were included. Results indicate that AI in UADT endoscopy achieves diagnostic accuracy, sensitivity, and specificity rates comparable to experts, with optimal outcomes combined with human expertise. AI also demonstrated significantly faster inference times.
conclusionsThis review highlights AI's potential to enhance clinical impact in UADT endoscopy, especially when combined with human expertise. However, the limited focus on real-time clinical translation underscores the need for further research to enable effective integration into clinical practice.
Indexed as
Identifiers
What Socratic 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 Socratic graph.