Bridging the AI implementation gap in otolaryngology: A clinical commentary

TL;DR

This paper discusses how AI can be applied in otolaryngology, highlighting challenges and offering a roadmap for its responsible integration into clinical practice.

Contribution

The paper provides a subspecialty-specific analysis of AI implementation challenges in otolaryngology and proposes a tailored roadmap for adoption.

Findings

AI implementation in otolaryngology faces unique workflow and regulatory challenges.

Subspecialty-driven validation and tailored reporting standards are essential for successful AI integration.

Otolaryngology can serve as a model for AI adoption in multidisciplinary clinical fields.

Abstract

Artificial intelligence (AI) is moving rapidly from research into specialty clinical care. Otolaryngology (ENT), deeply reliant on imaging, endoscopy, and complex multimodal diagnostics, is positioned to benefit substantially, but faces unique barriers to real-world AI adoption. While prior commentaries have highlighted general obstacles such as data diversity, workflow integration, and explainability, this manuscript examines how these challenges manifest specifically in ENT subspecialties. Focusing on cochlear implant (CI) mapping, vestibular diagnostics, and voice/speech rehabilitation, we detail the distinctive workflow, regulatory, and medico-legal issues of AI in ENT. We provide a roadmap for closing the implementation gap, emphasizing the need for subspecialty-driven validation, tailored reporting standards, and collaborative governance. Ultimately, the responsible integration of AI in otolaryngology can serve as a model for translating advanced technologies into procedural, multidisciplinary fields.