MHDash: An Online Platform for Benchmarking Mental Health-Aware AI Assistants

• URL: http://arxiv.org/abs/2602.00353v1
• Date: Fri, 30 Jan 2026 22:03:31 GMT
• Title: MHDash: An Online Platform for Benchmarking Mental Health-Aware AI Assistants
• Authors: Yihe Zhang, Cheyenne N Mohawk, Kaiying Han, Vijay Srinivas Tida, Manyu Li, Xiali Hei,
• Abstract summary: We present MHDash, an open-source platform designed to support the development, evaluation, and auditing of AI systems for mental health applications.<n>Our results reveal several key findings, including that simple baselines and advanced LLM APIs exhibit comparable overall accuracy yet diverge significantly on high-risk cases.<n>By releasing MHDash as an open platform, we aim to promote reproducible research, transparent evaluation, and safety-aligned development of AI systems for mental health support.
• Score: 2.89303424493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly applied in mental health support systems, where reliable recognition of high-risk states such as suicidal ideation and self-harm is safety-critical. However, existing evaluations primarily rely on aggregate performance metrics, which often obscure risk-specific failure modes and provide limited insight into model behavior in realistic, multi-turn interactions. We present MHDash, an open-source platform designed to support the development, evaluation, and auditing of AI systems for mental health applications. MHDash integrates data collection, structured annotation, multi-turn dialogue generation, and baseline evaluation into a unified pipeline. The platform supports annotations across multiple dimensions, including Concern Type, Risk Level, and Dialogue Intent, enabling fine-grained and risk-aware analysis. Our results reveal several key findings: (i) simple baselines and advanced LLM APIs exhibit comparable overall accuracy yet diverge significantly on high-risk cases; (ii) some LLMs maintain consistent ordinal severity ranking while failing absolute risk classification, whereas others achieve reasonable aggregate scores but suffer from high false negative rates on severe categories; and (iii) performance gaps are amplified in multi-turn dialogues, where risk signals emerge gradually. These observations demonstrate that conventional benchmarks are insufficient for safety-critical mental health settings. By releasing MHDash as an open platform, we aim to promote reproducible research, transparent evaluation, and safety-aligned development of AI systems for mental health support.

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