A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Design Principles and Insights for AI-Supported Care.

IF 5.8 2区 医学 Q1 PSYCHIATRY
Jmir Mental Health Pub Date : 2025-09-21 DOI:10.2196/75078
Sorio Boit, Rajvardhan Patil
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引用次数: 0

Abstract

Background: Artificial intelligence (AI), particularly large language models (LLMs), presents a significant opportunity to transform mental healthcare 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 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 (RAG) to ground responses in evidence-based therapies such as Cognitive Behavioral Therapy (CBT), Acceptance and Commitment Therapy (ACT), and Dialectical Behavior Therapy (DBT); and (3) a multi-tiered safety system, including a post-generation 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 FAITA-MH and READI 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.

Clinicaltrial:

基于大型语言模型的心理健康聊天机器人的快速工程框架:人工智能支持护理的设计原则和见解。
背景:人工智能(AI),特别是大型语言模型(llm),通过可扩展的按需支持,提供了一个重要的机会来改变精神卫生保健。虽然llm驱动的聊天机器人可能有助于减少护理障碍,但它们与临床环境的结合引发了对安全性、可靠性和道德监督的关键担忧。需要一个结构化的框架来获取它们的好处,同时处理固有的风险。本文介绍了一个快速工程的概念模型,概述了基于法学硕士的心理健康聊天机器人负责任开发的核心设计原则。目的:本文提出了一个综合的、分层的快速工程框架,该框架将循证治疗模型、适应性技术和伦理保障相结合。目标是提出并概述开发安全、有效和临床相关的人工智能驱动的精神卫生干预措施的实践基础。方法:我们概述了一个基于llm的心理健康聊天机器人的分层架构。该设计包含:(1)具有主动风险检测的输入层;(2)一个对话引擎,该引擎具有用于个性化和检索增强生成(RAG)的用户状态数据库,以响应基于证据的疗法,如认知行为疗法(CBT)、接受与承诺疗法(ACT)和辩证行为疗法(DBT);(3)多层安全系统,包括后一代道德过滤器和治疗师监督下的持续学习循环。结果:主要贡献在于框架本身,它将临床原则和伦理保障系统地嵌入到系统设计中。我们还提出了一种比较验证策略,以根据基线模型评估框架的附加价值。其组件明确映射到FAITA-MH和READI框架,确保与负责任的人工智能开发的当前学术标准保持一致。结论:该框架为法学硕士心理健康支持的负责任发展提供了实践基础。通过概述分层架构并使其与已建立的评估标准保持一致,这项工作为开发技术上有能力、安全、有效和合乎道德的人工智能工具提供了指导。未来的研究应优先通过本文介绍的阶段性比较方法对该框架进行实证验证。临床试验:
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Jmir Mental Health
Jmir Mental Health Medicine-Psychiatry and Mental Health
CiteScore
10.80
自引率
3.80%
发文量
104
审稿时长
16 weeks
期刊介绍: JMIR Mental Health (JMH, ISSN 2368-7959) is a PubMed-indexed, peer-reviewed sister journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR Mental Health focusses on digital health and Internet interventions, technologies and electronic innovations (software and hardware) for mental health, addictions, online counselling and behaviour change. This includes formative evaluation and system descriptions, theoretical papers, review papers, viewpoint/vision papers, and rigorous evaluations.
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