用AI构建动态心理数字孪生，实现注意力缺陷的个性化长期照护
Position: AI Will Transform Neuropsychology Through Mental Health Digital Twins for Dynamic Mental Health Care, Especially for ADHD
- 基于生成式AI持续采集患者日常体验数据
- 数字孪生模型可追踪个体症状变化轨迹，支持动态调整治疗
- 为精神科医生提供实时决策支持，适合临床研究与智能医疗团队
静态评估无法应对动态心智需求。我们主张从静态诊断转向由人工智能驱动的连续评估模式。以注意力缺陷多动障碍（ADHD）为例，探讨生成式AI如何突破神经心理学当前的能力瓶颈，实现更个性化的长期照护路径。具体而言，AI可高效开展频繁、低负担的经验抽样，并促进不同诊疗路径间的诊断一致性。未来有望通过持续、丰富且以患者为中心的数据采样，动态响应个体需求与病情演变，提升治疗的可及性与有效性。我们提出心理健康数字孪生（MHDT）——一种持续更新的计算模型，用于捕捉个体症状动态与演化轨迹——作为个性化心理健康照护的变革框架。该框架基于实证证据，已规划出推进其完善与落地的研究路线图。
原文摘要 · Abstract (English)
Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level experience sampling from patients and facilitate diagnostic reconciliation across care pathways. We envision a future where mental health care benefits from continuous, rich, and patient-centered data sampling to dynamically adapt to individual patient needs and evolving conditions, thereby improving both accessibility and efficacy of treatment. We further propose the use of mental health digital twins (MHDTs) - continuously updated computational models that capture individual symptom dynamics and trajectories - as a transformative framework for personalized mental health care. We ground this framework in empirical evidence and map out the research agenda required to refine and operationalize it.
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