{
  "id": 19560,
  "url": "https://www.jmir.org/2026/1/e91215",
  "title": "Advancing Evidence-Based Medicine for Population, Intervention, Comparison, and Outcome Element Recognition and Extraction in Medical Literature: Large Language Model Approach",
  "summary": "Background: The exponential expansion of biomedical literature has created an urgent need for efficient methods to recognize and extract population, intervention, comparison, and outcome (PICO) elements—the foundational elements of evidence-based medicine. Objective: This study systematically evaluated 2 complementary approaches for automating PICO recognition and extraction in medical literature: prompt engineering optimization and parameter-efficient fine-tuning (PEFT) of large language models",
  "authors": "Zeyuan Hao, Yifan Duan, Yu Wang",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T20:00:19.000Z",
  "fetched_at": "2026-08-15T05:10:17.122Z",
  "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/19560",
  "original_url": "https://www.jmir.org/2026/1/e91215",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}