Rethinking Evaluation Frameworks for AI in Mental Health Care
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[Submitted on 20 Jan 2026 (v1), last revised 28 Apr 2026 (this version, v2)]
A baker's-dozen of insight crammed into one ring.
Summary
This paper argues for a rethinking of how AI tools for mental health are evaluated, proposing an interdisciplinary framework that integrates clinical soundness, social context, and equity. Through analysis of 135 recent computational linguistics publications, the authors identify recurring limitations such as over-reliance on generic metrics that fail to capture clinical validity, limited involvement of mental health professionals, and insufficient attention to safety and equity. They propose a taxonomy of AI mental health support types—assessment-, intervention-, and information synthesis-oriented—each with distinct risks and evaluative requirements, illustrated through case studies.
Key quotes
· 3 pulledAlthough artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned with clinical practice, social context, and first-hand user experience.
This paper argues for a rethinking of responsible evaluation — what is measured, by whom, and for what purpose — by introducing an interdisciplinary framework that integrates clinical soundness, social context, and equity.
Through an analysis of 135 recent *CL publications, we identify recurring limitations, including over-reliance on generic metrics that do not capture clinical validity, therapeutic appropriateness, or user experience, limited participation from mental health professionals, and insufficient attention to safety and equity.
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