Conversational Large Language Models for Vestibular Diagnosis in Outpatient Clinics: Prospective Multicenter Diagnostic Accuracy Study
Background: Vestibular disorders are common, burdensome, and frequently misdiagnosed, particularly in nonspecialist settings where history-taking is often incomplete or inconsistently structured. Digital health tools that standardize symptom elicitation could improve diagnostic triage, but most existing systems rely on static questionnaires or rule-based logic. Large language models (LLMs) offer a more flexible alternative through adaptive, natural-language consultations, but prospective evidenc
Record details
Published: 7 August 2026
Source: JMIR (Journal of Medical Internet Research)
Category: Research
Topics: Healthcare
Retrieved: 8 August 2026
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How to cite this record
ethics.ai (7 August 2026), “Conversational Large Language Models for Vestibular Diagnosis in Outpatient Clinics: Prospective Multicenter Diagnostic Accuracy Study,” evidence record 17505, https://ethics.ai/record/17505 (originally published by JMIR (Journal of Medical Internet Research)).
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