Mental health assessments are of central importance to individuals' well-being. Conventional assessment methodologies predominantly depend on clinical interviews and standardised self-report questionnaires. Nevertheless, the efficacy of these methodologies is frequently impeded by factors such as subjectivity, recall bias, and accessibility issues. Furthermore, concerns regarding bias and privacy may result in misreporting in data collected through self-reporting in mental health research. The present study examined the design opportunities and challenges inherent in the development of a mental health assessment tool based on natural language interaction with large language models (LLMs). An interactive prototype system was developed using conversational AI for non-invasive mental health assessment, and was evaluated through semi-structured interviews with 11 mental health professionals (six counsellors and five psychiatrists). The analysis identified key design considerations for future development, highlighting how AI-driven adaptive questioning could potentially enhance the reliability of self-reported data while identifying critical challenges, including privacy protection, algorithmic bias, and cross-cultural applicability. This study provides an empirical foundation for mental health technology innovation by demonstrating the potential and limitations of natural language interaction in mental health assessment.

翻译：心理健康评估对个体福祉至关重要。传统评估方法主要依赖于临床访谈和标准化自陈式问卷。然而，这些方法的有效性常受主观性、回忆偏差和可及性等因素制约。此外，心理健康研究中通过自陈式收集的数据可能因偏见和隐私顾虑导致误报。本研究探讨了基于大型语言模型自然语言交互的心理健康评估工具开发所固有的设计机遇与挑战。我们利用对话式人工智能开发了一个用于非侵入式心理健康评估的交互式原型系统，并通过与11位心理健康专业人员（六名咨询师和五名精神科医生）的半结构化访谈进行评估。分析识别了未来开发的关键设计考量，揭示了人工智能驱动的自适应提问如何可能提升自陈数据的可靠性，同时指出了包括隐私保护、算法偏见和跨文化适用性在内的关键挑战。本研究通过展示自然语言交互在心理健康评估中的潜力与局限，为心理健康技术创新提供了实证基础。