Governing AI for Mental Health: Fragmented State Approaches and the Case for a Federal Framework

State-level regulation of AI used for mental health is emerging in the absence of a federal framework. States are taking different approaches to regulation, resulting in a fragmented regulatory landscape. This Viewpoint aims to identify the governance approaches that US states are using to regulate the use of AI in mental health and analyze the limitations of each. A 4-state case analysis was conducted using the statutory text of bills and laws in Illinois, Utah, New York, and Nevada. Two governance approaches were identified. The first regulates the use of AI in clinical contexts, and the second regulates the technology itself. Some states have combined elements of both approaches to address AI use more comprehensively. While these approaches aim to mitigate harm, they differ in where they believe risk lies in the use of AI for mental health support. The limitations of these divergent approaches include uneven protections for consumers and regulatory uncertainty for developers, vendors, deployers, and clinicians. Because AI in mental health operates across both clinical and consumer domains, neither approach alone can address the risks associated with its use for mental health support. A coordinated, risk-based federal regulatory floor is needed to ensure consistent protections across states.