{
  "id": 1199,
  "url": "https://arxiv.org/abs/2606.10796v1",
  "title": "Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning",
  "summary": "Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficulty in modeling complex but sparsely distributed depression clues within lengthy, multi-topic clinical interviews, leading to superficial and unreliable reasoning; 2) scarcity of labeled data due to clinical privacy, together with high cost of training and fine-tuning, limiting the deployment of supervised ADD systems.",
  "authors": "Yiqing Lyu, Xianbing Zhao, Buzhou Tang, Ronghuan Jiang",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-09T12:44:12.000Z",
  "fetched_at": "2026-07-14T14:15:03.617Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1199",
  "original_url": "https://arxiv.org/abs/2606.10796v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}