The Application and Diagnostic Accuracy of Artificial Intelligence in Rhinology: A Review

TL;DR

This paper reviews how artificial intelligence is being used in rhinology for tasks like image interpretation and diagnosis, showing promising accuracy but highlighting challenges in validation and clinical use.

Contribution

The study systematically evaluates AI diagnostic accuracy in rhinology, identifying key applications and barriers to clinical implementation.

Findings

AI image tools achieved 81%-99% accuracy in nasal polyp detection and CT scan interpretation.

ML models using patient data reached 74.5%-85.5% accuracy for chronic rhinosinusitis prediction.

Large language models like ChatGPT showed over 80% performance in clinical question answering.

Abstract

Artificial intelligence (AI) technologies, including machine learning (ML), deep learning, and large language models, are increasingly applied in medical diagnostics. In rhinology, these tools are being evaluated for tasks such as image interpretation, cytology classification, and clinical decision support. To systematically evaluate the application and diagnostic accuracy of AI technologies in rhinology, with a focus on clinical utility and implementation barriers. This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Seventeen full-text studies were screened based on predefined eligibility criteria, focusing on AI applications with diagnostic metrics in rhinology. Data on AI type, diagnostic task, performance outcomes, and study quality were extracted and synthesized narratively. Twelve studies met the inclusion criteria. Image-based diagnostic tools using convolutional neural networks demonstrated high accuracy (81%-99%) in nasal polyp detection, cytology classification, and computed tomography (CT) scan interpretation. ML models using patient-reported data achieved accuracies of 74.5%-85.5% for chronic rhinosinusitis prediction. Large language models like ChatGPT and Gemini were evaluated for clinical question answering, with performance exceeding 80% in some domains. Risk of bias was moderate in most primary studies, and none reported clinical integration beyond prototype stages. AI exhibits promising diagnostic accuracy across several applications in rhinology. However, significant challenges persist, including limited validation, methodological heterogeneity, and lack of clinical implementation. Future research should focus on prospective trials, explainability, and regulatory frameworks to ensure safe integration into clinical workflows.

Figures