ArticleDigital health
Bridging the AI implementation gap in otolaryngology: A clinical commentary.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A Pressure-Centered Mechanistic Framework for Precision Otology: The Neuro-Vascular-Mechanical-Inflammatory-Autonomic (NVMIA) Regulatory Architecture.Journal of personalized medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is moving rapidly from research into specialty clinical care. Otolaryngology (ENT), deeply reliant on imaging, endoscopy, and complex multimodal diagnostics, is positioned to benefit substantially, but faces unique barriers to real-world AI adoption. While prior commentaries have highlighted general obstacles such as data diversity, workflow integration, and explainability, this manuscript examines how these challenges manifest specifically in ENT subspecialties. Focusing on cochlear implant (CI) mapping, vestibular diagnostics, and voice/speech rehabilitation, we detail the distinctive workflow, regulatory, and medico-legal issues of AI in ENT. We provide a roadmap for closing the implementation gap, emphasizing the need for subspecialty-driven validation, tailored reporting standards, and collaborative governance. Ultimately, the responsible integration of AI in otolaryngology can serve as a model for translating advanced technologies into procedural, multidisciplinary fields.
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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.