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Dep-LLM uses LLMs for training-free depression diagnosis

Researchers have developed Dep-LLM, a novel framework for diagnosing depression from clinical interviews without requiring any additional training. This system leverages existing large language models (LLMs) by mimicking the structured reasoning process of psychiatrists. Dep-LLM analyzes lengthy dialogues, identifies key depression indicators, quantifies the reliability of its findings, and integrates these signals for a final diagnosis, outperforming both supervised and commercial LLMs on benchmark datasets. AI

IMPACT This method could enable more accessible and scalable AI-driven mental health diagnostics by leveraging existing LLMs without costly fine-tuning.

RANK_REASON The cluster contains a research paper detailing a new method for depression diagnosis using LLMs.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yiqing Lyu, Xianbing Zhao, Buzhou Tang, Ronghuan Jiang ·

    Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning

    arXiv:2606.10796v1 Announce Type: cross Abstract: 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 d…

  2. arXiv cs.AI TIER_1 English(EN) · Ronghuan Jiang ·

    Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning

    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 clinic…