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Cited by 34Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIDigitalization, Law, and Regulation

Governing AI in Mental Health: 50-State Legislative Review

Open access
Shumate, J Nicholas, Rozenblit, Eden, Flathers, Matthew, Larrauri, Carlos A, Hau, Christine, Xia, Winna, Torous, E Nicholas, Torous, John·2025·JMIR Mental Health
DOI 10.2196/80739PMC PMC12578431
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Abstract

Background: Mental health–related artificial intelligence (MH-AI) systems are proliferating across consumer and clinical contexts, outpacing regulatory frameworks and raising urgent questions about safety, accountability, and clinical integration. Reports of adverse events, including instances of self-harm and harmful clinical advice, highlight the risks of deploying such tools without clear standards and oversight. Federal authority over MH-AI is fragmented, leaving state legislatures to serve as de facto laboratories for MH-AI policy. Some states have been highly active in this area during recent legislative sessions. Yet, clinicians and professional organizations have mainly remained absent or sidelined from public commentary and policymaking bodies, raising concerns that new laws may diverge from the realities of mental health care. Objective: To systematically analyze recent state-level legislation relevant to MH-AI, categorize bills by relevance to mental health, identify major regulatory themes and gaps, and evaluate implications for clinicians and patients. Methods: We conducted a systematic analysis of bills introduced in all 50 US states between January 1, 2022, and May 19, 2025, using standardized searches on the legislative research website (LegiScan). Bills were screened and categorized using a custom 4-tier taxonomy based on their applicability to MH-AI. Bills passing threshold review were coded by topic using a 25-tag system developed through iterative consensus. Legally trained reviewers adjudicated final classifications to ensure consistency and rigor. Results: Among 793 state bills reviewed, 143 were identified as potentially impactful to MH-AI: 28 explicitly referenced mental health uses, while 115 had substantial or indirect implications. Of these 143 bills, 20 were enacted across 11 states. Legislative efforts varied widely, but 4 thematic domains consistently emerged: (1) professional oversight, including deployer liability and licensure obligations; (2) harm prevention, encompassing safety protocols, malpractice exposure, and risk stratification frameworks; (3) patient autonomy, particularly in areas of disclosure, consent, and transparency; and (4) data governance, with notable gaps in privacy protections for sensitive mental health data. Conclusions: State legislatures are rapidly shaping the regulatory landscape for MH-AI, but most laws treat mental health as incidental to broader artificial intelligence or health care regulation. Explicit mental health provisions remain rare, and clinician and patient perspectives are seldom incorporated into policymaking. The result is a fragmented and uneven environment that risks leaving patients unprotected and clinicians overburdened. Mental health professionals must proactively engage with legislators, professional organizations, and patient advocates to ensure that emerging frameworks address oversight, harm, autonomy, and privacy in ways that are clinically realistic, ethically sound, and supportive of flexible—but responsible—innovation.

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  • Several US states have passed laws to restrict AI use for mental health.

    “several US states have passed laws to restrict AI use for mental health28.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [28]

    The Abstract Results report 20 laws relevant to mental-health AI enacted across 11 states, supporting the claim that several states have passed relevant laws. But the Discussion’s Taxonomy Analysis says only two states had enacted laws explicitly addressing mental-health AI; the others affected it substantially or incidentally. The claim overstates how specifically those laws restrict AI use for mental health. (LLM, conf=0.84)

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Research works printed but not linked: 3

  1. [15]Kahane K François J Torous J Perspective: the digital health app policy landscape: regulatory gaps and choices through the lens of mental health J Ment Health Policy Econ Sep 1 2021 24 3 101 108 Medline 34554108
  2. [90]Health Insurance Portability and Accountability Act of 1996 Pub L No 104-191, § 264, 110 Stat 1936 URL https://www.govinfo.gov/content/pkg/PLAW-104publ191/pdf/PLAW-104publ191.pdf Accessed 20-10-2025 16477734
  3. [110]Governing AI in Mental Health: A 50-State Legislative Analysis URL https://governing-ai-in-mental-health.digitalpsychpapers.org/ Accessed 17-10-2025 PMC12578431 41172342

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Authors
Shumate, J. N., Rozenblit, E., Flathers, M., Larrauri, C. A., Hau, C., Xia, W., Torous, E. N., & Torous, J.
Affiliations
  • Beth Israel Deaconess Medical Center (US)
  • Harvard University (US)
Corresponding author
Shumate, J Nicholas
ORCID
Venue
JMIR Mental Health
Publisher
JMIR Publications Inc.
Published
2025-10-31
Volume
12
Pages
e80739-e80739
ISSN
2368-7959 (ISSN-L)
DOI
10.2196/80739
PMC
PMC12578431
OpenAlex
W4415773371
Cited by
33
References
10
Citations by year
2026: 31 · 2025: 2
FWCI
7.94
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Mental Health (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
state legislation, AI regulation, professional licensing, malpractice, patient autonomy, informed consent, data privacy, harm prevention, clinical implementation of AI, United States states, self-harm risk, risk stratification, disclosure transparency
MeSH terms
Artificial Intelligence, Humans, Mental Health, Mental Health Services, State Government, United States

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