AI Mental Health Evaluation Framework Proposed
A paper available on arXiv (2602.00065) introduces a novel interdisciplinary framework aimed at assessing AI tools in mental health care. The authors contend that existing evaluation approaches are disjointed and fail to align with clinical practices, social contexts, and user experiences. Through an analysis of 135 recent *CL publications, they highlight issues such as an excessive dependence on generic metrics, a lack of clinical validity, minimal engagement from mental health professionals, and insufficient focus on safety and equity. This new framework emphasizes clinical integrity, social relevance, and equity, while also presenting a taxonomy of AI support types in mental health, including both assessment and intervention, to facilitate responsible evaluation.
Key facts
- Paper arXiv:2602.00065 proposes a framework for evaluating AI in mental health.
- Current evaluation methods are fragmented and misaligned with clinical practice.
- Analysis of 135 *CL publications revealed over-reliance on generic metrics.
- Lack of clinical validity, therapeutic appropriateness, and user experience noted.
- Limited participation from mental health professionals in evaluations.
- Insufficient attention to safety and equity in current approaches.
- Framework integrates clinical soundness, social context, and equity.
- Taxonomy of AI mental health support types includes assessment and intervention.
Entities
Institutions
- arXiv