Governing AI in Mental Health: 50-State Legislative Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,099 | 0,253 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,034 | 0,032 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».