Vestibular disorders are common, heterogeneous, and diagnostically challenging. Nystagmus is an important sign in vestibular assessment, but its interpretation can depend on clinician expertise and recording quality. This structured narrative review mapped current evidence on artificial intelligence (AI)-enabled nystagmus and eye-movement signal analysis in vestibular assessment, focusing on study characteristics,...
✦ The floor
Discussion
Signed responses from readers of the wire.
No actionable change at this time — AI-enabled nystagmus analysis is still in the review/early-evidence stage and is not yet ready for routine clinical adoption.
Automating nystagmus analysis with AI could reduce diagnostic variability in vestibular assessment and improve access to accurate balance disorder evaluation across clinical settings.
- 01Review covers current evidence for AI-based analysis of nystagmus in vestibular disorder diagnosis.
- 02Heterogeneous presentations of vestibular disorders are a core diagnostic challenge AI aims to address.
- 03No single AI approach has emerged as the clinical standard; evidence base is still developing.
- 04Authors outline future research directions needed before clinical translation.
- 05Published in Journal of Vestibular Research, a peer-reviewed specialty journal.
AI-enabled nystagmus analysis shows promise for improving vestibular disorder assessment.
studypartially supportedHeterogeneous clinical presentations pose significant diagnostic challenges in vestibular assessment.
opinionsupported- PMID
- 42861970
- DOI
- 10.1177/09574271261496097.
- Journal
- Journal of Vestibular Research
- Publication type
- review
- Evidence level
- 2a
- Population
- Literature on patients undergoing vestibular assessment with nystagmus analysis
- Intervention
- AI-enabled nystagmus analysis systems
Primary outcomes
Diagnostic accuracy of AI-enabled nystagmus analysis; Current evidence gaps and future research directions