ReviewOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2026
Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways.
Review in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo critically evaluate advances in artificial intelligence (AI) within rhinology, focusing on translational readiness, regulatory alignment, and clinical implementation pathways. DATA SOURCES: PubMed and Scopus. REVIEW
methodsPubMed and Scopus were searched using predefined Title/Abstract Boolean strategies with publication date (January 1, 2023-February 1, 2025), English-language, and human-study restrictions to support a structured state-of-the-art narrative synthesis. Major application domains were categorized as automated computed tomography (CT) analysis, endoscopic computer vision, phenotyping and endotyping in chronic rhinosinusitis, outcome prediction using patient-reported data, digital olfaction, and patient-facing language tools. Each domain was assessed for validation status, workflow feasibility, equity, and data-economics considerations, and alignment with regulatory pathways for software as a medical device.
conclusionsAutomated sinus CT models have achieved multi-institutional external validation and appear closest to workflow translation. Endoscopic systems demonstrate promising performance in near real-time video but remain largely retrospective and require evaluation in live workflows. Predictive modeling using integrated clinical, molecular, or patient-reported data remains exploratory, while digital olfaction and language models lack standardized validation and regulatory oversight. Across domains, implementation barriers persist, including interoperability with electronic health records, economic disincentives to data aggregation, and risks of inequitable performance. IMPLICATIONS FOR PRACTICE: AI in rhinology is progressing toward integration into clinical care, with automated imaging applications leading adoption. Responsible deployment requires prospective multicenter trials, clinician-supervised workflows, transparency in performance across demographic groups, and evidence of improved patient outcomes. A structured readiness framework may guide stakeholders in prioritizing regulatory-feasible tools that offer measurable clinical value.
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Identifiers
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.