用智能代理AI提升阿尔茨海默病诊疗效率与可及性
Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease
- 构建六阶段路线图，实现AI在神经退行性疾病诊疗中的负责任集成
- 通过多模态数据标准化与临床反馈持续优化，提升诊断准确性
- 聚焦医生能力增强与患者安全，适用于多种医疗系统压力场景
美国医疗系统难以应对阿尔茨海默病及相关痴呆症（ADRD）日益增长的诊疗需求。基于语言模型的生成式AI现可构建智能代理系统，增强临床医生能力，实现大规模、接近专科水平的ADRD评估与决策。本文提出一个涵盖六个阶段的综合路线图：(1) 多模态高质量标准化数据采集；(2) 决策支持；(3) 临床工作流程整合；(4) 严格的验证与监控机制；(5) 通过临床反馈实现持续学习；(6) 坚固的伦理与风险管理框架。该以人为本的方法优化了医生在全面数据收集、复杂临床信息解读及及时应用医学知识方面的能力，同时优先保障患者安全、医疗公平与透明性。尽管聚焦于ADRD，这些原则对面临类似系统挑战的其他医学领域亦具广泛适用性。
原文摘要 · Abstract (English)
United States healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias (ADRD). Generative AI built on language models (LLMs) now enables agentic AI systems that can enhance clinician capabilities to approach specialist-level assessment and decision-making in ADRD care at scale. This article presents a comprehensive six-phase roadmap for responsible design and integration of such systems into ADRD care: (1) high-quality standardized data collection across modalities; (2) decision support; (3) clinical integration enhancing workflows; (4) rigorous validation and monitoring protocols; (5) continuous learning through clinical feedback; and (6) robust ethics and risk management frameworks. This human centered approach optimizes clinicians' capabilities in comprehensive data collection, interpretation of complex clinical information, and timely application of relevant medical knowledge while prioritizing patient safety, healthcare equity, and transparency. Though focused on ADRD, these principles offer broad applicability across medical specialties facing similar systemic challenges.
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