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New AI framework enhances explainable depression symptom annotation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel framework to improve the annotation of depression symptoms for AI systems, addressing the common issue of labels lacking structured evidence or clear alignment with diagnostic criteria. This self-evolving, expert-in-the-loop system combines large language model assistance with human verification to create more reliable and explainable datasets for mental health research. The framework operates in three stages, including evidence selection, DSM-5-TR analysis, and case-level synthesis, and features a dual-memory architecture to internalize expert feedback for iterative improvement without retraining. AI

IMPACT This framework could lead to more reliable and interpretable AI models for mental health research by improving the quality of training data.

RANK_REASON The cluster contains an academic paper detailing a new framework for AI annotation.

Read on arXiv cs.MA (Multiagent) →

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

New AI framework enhances explainable depression symptom annotation

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The cluster contains an academic paper detailing a new framework for AI annotation.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hoang-Loc Cao, Van Pham, Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Phuc Ho, Veronica Whitford, Hung Cao ·

    Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation

    arXiv:2607.15202v1 Announce Type: new Abstract: Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, s…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Hung Cao ·

    Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation

    Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignme…

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