Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
arXiv:2606.10796v1 Announce Type: new 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 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. To jointly address these challenges, we propose Dep
The proliferation of advanced large language models (LLMs) and the increasing focus on mental health solutions are converging, enabling new methodologies for sensitive diagnostic tasks without extensive custom training data.
This development indicates a significant step towards practical and scalable AI-driven diagnostic tools in healthcare, particularly in sensitive areas where data scarcity and privacy are major barriers.
Traditional reliance on large, annotated datasets for training specialized medical AI is potentially diminished, allowing for the faster deployment of diagnostic tools via general-purpose LLMs.
- · AI healthcare solution providers
- · Mental health professionals
- · Patients seeking accessible diagnosis
- · Developers of general-purpose LLMs
- · Companies reliant on bespoke, data-intensive AI training for diagnosis
- · Specialized annotation services for medical data
Wider adoption of AI for mental health screening and early diagnosis.
Increased pressure on regulatory bodies to establish clear frameworks for AI in sensitive medical diagnostics, altering liability and ethical considerations.
Potential for integration of AI diagnostics into primary care settings, fundamentally changing the patient entry point for mental health support and potentially exacerbating existing healthcare disparities if not carefully managed.
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