{
  "id": 11068,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1850832",
  "title": "MMCRAG-Resp: a multi-modal corrective retrieval-augmented generation framework for explainable respiratory disease reasoning",
  "summary": "BackgroundStandard Retrieval-Augmented Generation (RAG) systems only use semantic similarity to retrieve information, and since this method is quite limiting, it may find clinically irrelevant evidence and produce outputs that are unsafe or hallucinated. This drawback is particularly important in respiratory care, where the diagnosis relies heavily on very accurate physiological indicators such as spirometry patterns and symptom profiles.MethodsWe propose MMCRAG-Resp., a clinically grounded, phy",
  "authors": "A. Anny Leema",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-16T00:00:00.000Z",
  "fetched_at": "2026-07-17T05:10:53.887Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/11068",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1850832",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}