and Objectives : Artificial intelligence (AI) has generated numerous proof-of-concept applications in otology and neurotology, but relatively few have progressed to routine clinical use. This narrative review evaluates not only what AI can do across audiovestibular medicine, but also how close individual applications are to clinical translation and which barriers continue to limit implementation....
✦ The floor
Discussion
Signed responses from readers of the wire.
No actionable clinical change — this is a narrative review identifying research priorities; no AI tool for otology or neurotology has yet demonstrated sufficient real-world validation to alter current clinical workflows.
Understanding the barriers to AI adoption in otology and neurotology is essential for guiding investment, research design, and realistic timelines for clinically deployable AI tools in hearing healthcare.
- 01Reviews the translational gap between AI research prototypes and clinical deployment in ear/hearing medicine.
- 02Identifies key limitations: data quality, lack of prospective validation, regulatory hurdles, and explainability.
- 03Outlines research priorities needed before AI tools can be considered clinically ready.
- 04Covers both otology (ear conditions) and neurotology (hearing nerve and balance disorders).
- 05No new AI tool is introduced or tested — this is a critical landscape review.
There is a significant translational gap between AI proof-of-concept applications and routine clinical use in otology and neurotology.
opinionpartially supported- PMID
- 42942426
- DOI
- 10.3390/medicina62101895.
- Journal
- Medicina
- Publication type
- review
- Evidence level
- 5
- Population
- AI applications and studies in otology and neurotology (literature review, no patient population)
- Intervention
- Review of AI applications in otology and neurotology
Primary outcomes
Assessment of clinical readiness of AI tools in otology and neurotology; Identification of key limitations and research priorities for AI translation