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Governing AI in Mental Health: 50-State Legislative Review.

J Nicholas Shumate1, Eden Rozenblit1, Matthew Flathers1

  • 1Division of Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Rabb-2, Boston, 02215, United States, 1 617-667-6700 ext 11067.

JMIR Mental Health
|October 31, 2025
PubMed
Summary

State legislatures are creating regulations for mental health artificial intelligence (MH-AI) but often overlook specific mental health needs. Proactive engagement from mental health professionals is crucial for developing effective and ethical MH-AI governance.

Keywords:
AI oversightAI regulationAI riskAI safetyLLMMH-AIalgorithmic biasartificial intelligencechatbotclinical oversightdigital companionsdigital healthdigital therapydigital treatmentsgenerative AIhealth lawhealthcare policylarge language modellawlegislationlegislative trendsliabilitymachine learningmalpracticemedical regulationmental health–related artificial intelligencepolicy analysispsychiatric toolsregulatory gapstechnology ethicstherapy bots

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Area of Science:

  • Health Law
  • Artificial Intelligence
  • Mental Health Policy

Background:

  • Mental health artificial intelligence (MH-AI) systems are rapidly expanding, outpacing regulatory frameworks and raising safety and accountability concerns.
  • Adverse events and fragmented federal authority highlight the need for clear MH-AI standards, with states emerging as key policy developers.
  • Clinician and professional organization absence from policymaking risks laws diverging from clinical realities.

Purpose of the Study:

  • To systematically analyze state legislation impacting MH-AI.
  • To categorize bills by relevance to mental health and identify key regulatory themes and gaps.
  • To evaluate the implications of these laws for clinicians and patients.

Main Methods:

  • Systematic analysis of 793 state bills introduced between January 2022 and May 2025.
  • Bills screened and categorized using a custom 4-tier taxonomy for MH-AI relevance.
  • Enacted bills coded by topic using a 25-tag system, with final classifications adjudicated by legal experts.

Main Results:

  • 143 of 793 bills (20 enacted in 11 states) were identified as potentially impacting MH-AI.
  • Key legislative themes include professional oversight, harm prevention, patient autonomy, and data governance.
  • Significant gaps exist in privacy protections for sensitive mental health data, and explicit mental health provisions are rare.

Conclusions:

  • State legislatures are actively shaping MH-AI regulation, but often treat mental health incidentally.
  • The lack of clinician and patient input results in fragmented regulations that may not be clinically realistic or ethically sound.
  • Mental health professionals must engage in policymaking to ensure regulations support responsible MH-AI innovation while protecting patients and clinicians.