{
  "id": 6705,
  "url": "https://arxiv.org/abs/2603.25322v1",
  "title": "AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study",
  "summary": "Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and patient demographics. Although large language models (LLMs) have shown promise in biomedicine, their use in AD has largely been confined to answering narrow, disease-specific questions rather than generating ",
  "authors": "Wenlong Hou, Sheng Bi, Guangqian Yang, Lihao Liu, Ye Du, Hanxiao Xue et al.",
  "category": "research",
  "topics": "bias-fairness,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-26T11:10:01.000Z",
  "fetched_at": "2026-07-14T16:32:41.666Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6705",
  "original_url": "https://arxiv.org/abs/2603.25322v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}