Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared...

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.
• 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.