#mental-health-ai News & Analysis

3 articles tagged with #mental-health-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · May 127/10

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Mental Health AI Safety Claims Must Preserve Temporal Evidence

Researchers argue that current mental health AI safety evaluations fail to detect clinically significant failures because they assess isolated responses rather than temporal patterns across conversations. The paper introduces Temporal Safety Non-Identifiability to formalize why sequence-dependent failures cannot be certified by turn-level evaluations, proposing SCOPE-MH as a new evaluation standard that preserves conversation history and cumulative effects.

AINeutralarXiv – CS AI · Apr 107/10

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Blending Human and LLM Expertise to Detect Hallucinations and Omissions in Mental Health Chatbot Responses

Researchers demonstrate that standard LLM-as-a-judge methods achieve only 52% accuracy in detecting hallucinations and omissions in mental health chatbots, failing in high-risk healthcare contexts. A hybrid framework combining human domain expertise with machine learning features achieves significantly higher performance (0.717-0.849 F1 scores), suggesting that transparent, interpretable approaches outperform black-box LLM evaluation in safety-critical applications.

AIBullisharXiv – CS AI · May 126/10

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New AI-Driven Tools for Enhancing Campus Well-being: A Prevention and Intervention Approach

Researchers have developed an integrated AI framework for campus mental health monitoring, combining TigerGPT (an LLM-powered survey chatbot) for prevention and PsychoGPT (a DSM-5-aligned screening tool) for intervention. The system uses reinforcement learning and multi-model reasoning to improve feedback quality and reduce hallucinations in mental health assessment.