Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways

Area of Science:

• Rhinology
• Artificial Intelligence
• Medical Informatics

Background:

• Artificial intelligence (AI) is rapidly advancing, with significant potential to transform rhinology.
• Evaluating the translational readiness and clinical implementation of AI tools is crucial for responsible adoption.

Purpose of the Study:

• To critically evaluate AI advancements in rhinology.
• Focus on translational readiness, regulatory alignment, and clinical implementation pathways.

Main Methods:

• Systematic literature search of PubMed and Scopus (January 2023-February 2025).
• Categorization of AI applications: CT analysis, endoscopic computer vision, chronic rhinosinusitis phenotyping, outcome prediction, digital olfaction, and language tools.
• Assessment of validation, workflow feasibility, equity, data-economics, and regulatory pathways for each domain.

Main Results:

• Automated CT analysis models show multi-institutional validation, nearing workflow translation.
• Endoscopic AI demonstrates real-time potential but requires live workflow evaluation.
• Predictive modeling, digital olfaction, and language tools are largely exploratory with limited validation and oversight.
• Implementation barriers include EHR interoperability, data aggregation economics, and equity concerns.

Conclusions:

• AI is progressing into rhinology clinical care, led by automated imaging.
• Responsible deployment necessitates prospective trials, clinician oversight, transparent performance metrics, and demonstrated patient outcomes.
• A structured readiness framework can guide the prioritization of clinically valuable and regulatory-feasible AI tools.