心理健康对话智能体的整合性综述：连接计算机科学与医学视角
An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
AI总结：
本文通过PRISMA框架系统综述了534篇跨计算机科学与医学的心理健康对话智能体论文，揭示了两领域在方法上的差异，并提出促进跨学科发展的建议。
摘要：
心理健康对话智能体（又称聊天机器人）因其为经历心理健康挑战的人群提供可及支持的潜力而被广泛研究。此前关于该主题的综述主要只考虑发表在计算机科学或医学其中一个领域的论文，导致两个领域之间在理解上存在割裂，并阻碍了有益知识的共享。为弥合这一差距，我们采用PRISMA框架进行了一项全面的文献综述，审阅了同时发表于计算机科学和医学领域的534篇论文。我们的系统综述揭示了136篇关于构建心理健康相关对话智能体的关键论文，这些论文在建模和实验设计技术上具有多样化的特征。我们发现，计算机科学论文侧重于LLM技术，并使用自动化指标评估回复质量，而对实际应用关注较少；医学论文则使用基于规则的对话智能体和结果指标来衡量参与者的健康结果。基于我们在本综述中对透明度、伦理和文化异质性方面的发现，我们提出了若干建议，以帮助弥合学科鸿沟，并促进心理健康对话智能体的跨学科发展。
英文摘要：
Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
