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Related Experiment Video

Updated: Jun 9, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared

Hannah Lee1, Rebecca Handler1, Tushar Mungle2

  • 1School of Medicine, Stanford University, Stanford, CA 94305, United States.

Journal of the American Medical Informatics Association : JAMIA
|May 20, 2026
PubMed
Summary

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Generative artificial intelligence (AI) chatbots in mental health lack safety standards. A governance framework with transparency and oversight is crucial for safe AI deployment in mental healthcare.

Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Mental Health Technology

Background:

  • Generative AI chatbots are increasingly used in mental healthcare.
  • These large language models offer human-like support but lack safety and effectiveness evidence.
  • Current deployment outpaces regulatory and clinical validation.

Purpose of the Study:

  • To identify risks associated with AI mental health chatbots.
  • To propose a governance framework for safe, accountable, and equitable AI deployment.
  • To address the gap between AI capabilities and clinical validation.

Main Methods:

  • Systematic synthesis of clinical, regulatory, and behavioral health literature.
  • Analysis of reported harms and system failure modes of AI chatbots.
Keywords:
accountabilityartificial intelligence mental-health chatbotsclinical safetyevaluation frameworkstransparency

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  • Development of a three-stage safety framework for AI mental health tools.

    • Main Results:

    • Significant governance gaps exist in the deployment of AI mental health chatbots.
    • AI chatbots present risks due to a lack of standardized safety and effectiveness evaluation.
    • A multi-stage framework is needed to manage risks throughout the AI lifecycle.

    Conclusions:

    • Transparency, standardized evaluation, and ongoing oversight are essential for AI mental health chatbots.
    • Shared responsibility among developers, regulators, clinicians, and researchers is critical.
    • Ensuring AI systems support, rather than harm, mental health requires robust governance.