ArticleAnnals of medicine2026
Artificial intelligence-assisted real-time nasopharyngeal cancer diagnostic model enhances rhinologist performance: a prospective multi-reader study.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Response to letter regarding "artificial intelligence-assisted real-time nasopharyngeal cancer diagnostic model enhances rhinologist performance: a prospective multi-reader study".Annals of medicine · 2026Article
- Regarding 'artificial intelligence-assisted real-time nasopharyngeal cancer diagnostic model enhances rhinologist performance: a prospective multi-reader study'.Annals of medicine · 2026Article
- Integrated bioinformatics and experimental validation identifies CLIC6 as a novel tumor suppressor regulating NF-κB signaling and immune microenvironment in nasopharyngeal carcinoma.Translational oncology · 2026Article
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Authors and funding
12 authors.
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Abstract
backgroundNasopharyngeal carcinoma (NPC) poses significant diagnostic challenges due to the anatomical complexity of the nasopharynx and reliance on endoscopic visual interpretation, often leading to delayed detection and unnecessary biopsies. Although artificial intelligence (AI) algorithms have shown promise in enhancing endoscopic cancer diagnosis, their real-world impact on clinician diagnostic performance remains insufficiently characterized.
methodsIn this prospective, multi-reader study, 47 clinicians, including experts, residents, and trainees, interpreted 200 nasoendoscopic images from 100 patients with histopathologically confirmed NPC or benign lesions. Each participant completed two diagnostic sessions: an unassisted evaluation and an AI-assisted assessment using a real-time lesion-annotating model (NPC-SDNet), with a 4-week washout period between sessions.
resultsWithout AI support, the overall diagnostic accuracy was 73.6% (95% CI: 70.1-77.0%), with a sensitivity of 76.1% (95% CI: 70.4-81.4%) and a specificity of 69.9% (95% CI: 63.8-76.0%). AI assistance significantly improved accuracy to 85.6% (95% CI: 83.1-87.6%,
conclusionsAI-assisted nasoendoscopic evaluation significantly enhances diagnostic accuracy, efficiency and interobserver consistency, particularly among less-experienced clinicians. These findings support the clinical integration of real-time AI tools to augment NPC recognition and standardize diagnostic performance across varying expertise levels.
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