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