โก Quick Summary
This article introduces the Mental Well-Being Through Dialogue – Safeguarded and Adaptive Framework for Ethics (MIND-SAFE), a structured approach for developing large language model (LLM)-based mental health chatbots. The framework aims to ensure that these AI-driven tools are safe, effective, and ethically sound while integrating evidence-based therapeutic models.
๐ Key Details
- ๐ Framework Name: MIND-SAFE
- ๐งฉ Core Components: Input layer, dialogue engine, multitiered safety system
- โ๏ธ Therapeutic Models: Cognitive Behavioral Therapy, Acceptance and Commitment Therapy, Dialectical Behavior Therapy
- ๐ Validation Strategy: Comparative validation against baseline models
๐ Key Takeaways
- ๐ค AI in Mental Health: LLMs can provide scalable, on-demand support for mental health care.
- ๐ Ethical Oversight: The framework emphasizes the importance of ethical safeguards in AI development.
- ๐ Layered Architecture: The design includes proactive risk detection and personalized dialogue management.
- ๐ง Evidence-Based Therapy: Responses are grounded in established therapeutic practices.
- ๐ Continuous Learning: The system incorporates a learning loop with therapist oversight for ongoing improvement.
- ๐ Alignment with Standards: The framework aligns with current scholarly standards for responsible AI development.
- ๐ Future Research: Empirical validation of the framework is essential for its practical application.
๐ Background
The integration of artificial intelligence (AI) into mental health care presents a transformative opportunity to enhance accessibility and support. However, the deployment of large language model (LLM)-powered chatbots raises significant concerns regarding their safety, reliability, and ethical implications. A structured framework is essential to harness the benefits of these technologies while mitigating risks.
๐๏ธ Study
The authors propose the MIND-SAFE framework, which serves as a comprehensive guide for the responsible development of LLM-based mental health chatbots. This framework incorporates evidence-based therapeutic models and ethical safeguards, aiming to create AI-driven interventions that are both clinically relevant and safe for users.
๐ Results
The primary contribution of this study is the MIND-SAFE framework itself, which systematically integrates clinical principles and ethical considerations into the design of mental health chatbots. The proposed validation strategy aims to evaluate the framework’s effectiveness compared to baseline models, ensuring its alignment with established standards for AI in mental health.
๐ Impact and Implications
The MIND-SAFE framework has the potential to revolutionize the development of mental health support tools. By embedding ethical safeguards and clinical principles into AI systems, we can enhance the trustworthiness and effectiveness of LLM-based chatbots. This could lead to improved mental health outcomes and greater accessibility to care for individuals in need.
๐ฎ Conclusion
The introduction of the MIND-SAFE framework marks a significant step towards the responsible integration of AI in mental health care. By prioritizing safety, effectiveness, and ethical considerations, this framework provides a solid foundation for future research and development in the field. Continued exploration and empirical validation will be crucial to realizing the full potential of AI-driven mental health interventions.
๐ฌ Your comments
What are your thoughts on the integration of AI in mental health care? Do you believe frameworks like MIND-SAFE can address the ethical concerns associated with AI? ๐ฌ Share your insights in the comments below or connect with us on social media:
A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework.
Abstract
BACKGROUND: Artificial intelligence (AI), particularly large language models (LLMs), presents a significant opportunity to transform mental health care through scalable, on-demand support. While LLM-powered chatbots may help reduce barriers to care, their integration into clinical settings raises critical concerns regarding safety, reliability, and ethical oversight. A structured framework is needed to capture their benefits while addressing inherent risks. This paper introduces a conceptual model for prompt engineering, outlining core design principles for the responsible development of LLM-based mental health chatbots.
OBJECTIVE: This paper proposes the Mental Well-Being Through Dialogue – Safeguarded and Adaptive Framework for Ethics (MIND-SAFE), a comprehensive, layered framework for prompt engineering that integrates evidence-based therapeutic models, adaptive technology, and ethical safeguards. The objective is to propose and outline a practical foundation for developing AI-driven mental health interventions that are safe, effective, and clinically relevant.
METHODS: We outline a layered architecture for an LLM-based mental health chatbot. The design incorporates (1) an input layer with proactive risk detection; (2) a dialogue engine featuring a user state database for personalization and retrieval-augmented generation to ground responses in evidence-based therapies such as cognitive behavioral therapy, acceptance and commitment therapy, and dialectical behavior therapy; and (3) a multitiered safety system, including a postgeneration ethical filter and a continuous learning loop with therapist oversight.
RESULTS: The primary contribution is the framework itself, which systematically embeds clinical principles and ethical safeguards into system design. We also propose a comparative validation strategy to evaluate the framework’s added value against a baseline model. Its components are explicitly mapped to the Framework for AI Tool Assessment in Mental Health and Readiness Evaluation for AI-Mental Health Deployment and Implementation frameworks, ensuring alignment with current scholarly standards for responsible AI development.
CONCLUSIONS: The framework offers a practical foundation for the responsible development of LLM-based mental health support. By outlining a layered architecture and aligning it with established evaluation standards, this work offers guidance for developing AI tools that are technically capable, safe, effective, and ethically sound. Future research should prioritize empirical validation of the framework through the phased, comparative approach introduced in this paper.
Author: [‘Boit S’, ‘Patil R’]
Journal: JMIR Ment Health
Citation: Boit S and Patil R. A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework. A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework. 2025; 12:e75078. doi: 10.2196/75078