Bridging the AI implementation gap in otolaryngology: A clinical commentary

Area of Science:

• Otolaryngology
• Medical Informatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is increasingly transitioning from research to clinical applications.
• Otolaryngology (ENT), a field reliant on diagnostics and imaging, stands to gain significantly from AI.
• Unique barriers hinder AI adoption in clinical ENT practice.

Purpose of the Study:

• To examine the specific challenges of AI adoption within ENT subspecialties.
• To detail the distinct workflow, regulatory, and medico-legal issues in AI for ENT.
• To propose a roadmap for bridging the AI implementation gap in otolaryngology.

Main Methods:

• Analysis of AI implementation challenges in cochlear implant (CI) mapping.
• Review of AI integration issues in vestibular diagnostics.
• Examination of AI adoption barriers in voice and speech rehabilitation.

Main Results:

• General AI obstacles like data diversity and workflow integration present unique forms in ENT subspecialties.
• Cochlear implant mapping, vestibular diagnostics, and voice rehabilitation face specific implementation hurdles.
• Distinct workflow, regulatory, and medico-legal considerations are critical for AI in ENT.

Conclusions:

• Responsible AI integration in otolaryngology requires subspecialty-specific validation and tailored standards.
• Collaborative governance is essential for overcoming AI adoption barriers in ENT.
• Otolaryngology can serve as a model for AI translation in other procedural, multidisciplinary medical fields.