Toward a Living Meta-Framework for Evaluating Generative AI in Mental Health

The proliferation of evaluative approaches for generative AI tools used for mental health purposes reflects much-needed attention to safety, ethics, and emerging risks. However, limited alignment across frameworks complicates comparisons, slows progress toward shared goals, and provides little clarity for regulatory processes. The result is a fragmented field that lacks cohesive guidance as the technology and its real-world patterns of use continue to evolve. This position paper synthesizes current evaluative approaches, outlines sources of divergence across existing frameworks, and identifies shared domains that could serve as anchors for coordinated development. We advocate for a living, flexible meta-framework maintained by a global, cross-sector consortium that can harmonize criteria, support consistent evaluation across jurisdictions, and adapt alongside scientific, clinical, and technological advances. Such a structure can strengthen safety, transparency, and methodological consistency while giving researchers, clinicians, developers, and policymakers a shared foundation for assessing generative AI tools in mental health care.