{
  "id": 18087,
  "url": "https://www.jmir.org/2026/1/e84086",
  "title": "Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study",
  "summary": "Background: Adverse drug events (ADEs) pose significant public health challenges and economic burdens. While substantial ADE information is documented in unstructured clinical notes, its extraction remains difficult due to semantic complexity. Large language models (LLMs) offer promising text comprehension capabilities but are often hindered by domain-specific hallucinations. Objective: This study aims to evaluate the effectiveness of retrieval-augmented generation (RAG) in improving the identif",
  "authors": "Junlong Ma, Xuehong Wu, Zeying Feng, Yun Kuang, Zhendong Ding, Min Li, Guoping Yang",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T18:30:15.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "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/18087",
  "original_url": "https://www.jmir.org/2026/1/e84086",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}