用大模型把技术解释转成医生能用的临床建议。
From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screening
- 用大模型把SHAP、LIME等工具的输出翻译成临床可读文本。
- 生成的内容结合临床指南，具备证据支持且可直接用于诊疗流程。
- 适合临床医生、研究人员及推动AI落地的心理健康领域从业者。
可解释人工智能（XAI）被视为释放机器学习在心理健康筛查中潜力的关键。然而，实验室到临床的鸿沟依然存在。当前的XAI技术（如SHAP、LIME）虽能生成特征重要性等技术性输出，却难以提供临床可用、患者可理解的行动建议。这种技术透明性与人类实用性之间的脱节，是阻碍实际应用的主要障碍。本文认为这一问题本质是‘翻译’问题，提出生成式操作框架（Generative Operational Framework），以大语言模型（LLM）为核心翻译引擎，接收多种XAI工具的原始输出，并通过检索增强生成（RAG）融合临床指南，自动生成可读性强、有证据支撑的临床叙事。我们系统分析了该框架整合的组件，追溯从内在模型到生成式XAI的发展脉络，证明其能有效解决工作流集成、偏见缓解及多方沟通等关键运营挑战。论文还为领域提供了战略路线图，推动从孤立数据点生成迈向可操作、可信的临床智能服务。
原文摘要 · Abstract (English)
Explainable Artificial Intelligence (XAI) has been presented as the critical component for unlocking the potential of machine learning in mental health screening (MHS). However, a persistent lab-to-clinic gap remains. Current XAI techniques, such as SHAP and LIME, excel at producing technically faithful outputs such as feature importance scores, but fail to deliver clinically relevant, actionable insights that can be used by clinicians or understood by patients. This disconnect between technical transparency and human utility is the primary barrier to real-world adoption. This paper argues that this gap is a translation problem and proposes the Generative Operational Framework, a novel system architecture that leverages Large Language Models (LLMs) as a central translation engine. This framework is designed to ingest the raw, technical outputs from diverse XAI tools and synthesize them with clinical guidelines (via RAG) to automatically generate human-readable, evidence-backed clinical narratives. To justify our solution, we provide a systematic analysis of the components it integrates, tracing the evolution from intrinsic models to generative XAI. We demonstrate how this framework directly addresses key operational barriers, including workflow integration, bias mitigation, and stakeholder-specific communication. This paper also provides a strategic roadmap for moving the field beyond the generation of isolated data points toward the delivery of integrated, actionable, and trustworthy AI in clinical practice.
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