Artificial intelligence-assisted real-time nasopharyngeal cancer diagnostic model enhances rhinologist performance: a prospective multi-reader study.

This project focuses on the development and comprehensive evaluation of an artificial intelligence-assisted diagnostic model specifically engineered for the real-time detection of nasopharyngeal cancer (NPC). The core objective involves designing and implementing a robust deep learning system capable of processing relevant medical data, such as endoscopic images or other clinical indicators, to provide immediate diagnostic support. The research aims to demonstrate how the integration of this AI model can significantly enhance the diagnostic performance of rhinologists, improving both accuracy and efficiency in identifying NPC. The methodology encompasses the architectural design of a deep learning model optimized for rapid inference in a clinical environment, ensuring its suitability for real-time application. A

critical component of this study involves a prospective multi-reader evaluation, where the diagnostic capabilities of multiple clinicians are assessed both independently and with the aid of the AI system. This comparative analysis seeks to quantify the tangible benefits of AI integration in clinical workflows, aiming to reduce diagnostic variability and improve patient outcomes through advanced, real-time decision support for NPC diagnosis.

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