{
  "id": 17505,
  "url": "https://www.jmir.org/2026/1/e100442",
  "title": "Conversational Large Language Models for Vestibular Diagnosis in Outpatient Clinics: Prospective Multicenter Diagnostic Accuracy Study",
  "summary": "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",
  "authors": "Chongkai Lu, Ruiqi Zhang, Huaili Jiang, Yanping Yu, Sulin Zhang, Qin Lin, Peixia Wu, Huawei Li",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T20:30:12.000Z",
  "fetched_at": "2026-08-08T05:10:34.355Z",
  "source_slug": "x-jmir-journal-of-medical-internet-researc",
  "source_name": "JMIR (Journal of Medical Internet Research)",
  "source_homepage": "https://www.jmir.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17505",
  "original_url": "https://www.jmir.org/2026/1/e100442",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}