AgenticAD：面向阿尔茨海默病全面管理的专业化多智能体系统框架
AgenticAD: A Specialized Multiagent System Framework for Holistic Alzheimer Disease Management
AI总结：
本文提出AgenticAD框架，利用八个专业化AI智能体协作，整合照护支持、数据分析与多模态工作流，以实现阿尔茨海默病的全面管理，改善患者预后并减轻照护负担。
摘要：
阿尔茨海默病（AD）对患者、照护者及医疗保健系统构成复杂且多方面的挑战，亟需整合且动态的支持解决方案。尽管人工智能（AI）为干预提供了有前景的途径，但当前的应用往往是孤立的，仅针对疾病的单一层面（如诊断或照护者支持），缺乏系统性整合。本文提出一种新颖的方法论框架，用于构建一个旨在实现阿尔茨海默病全面管理的综合性多智能体系统（MAS）。其目标是详细阐述一个由专业化AI智能体组成的协作生态系统的架构，每个智能体被设计用于应对AD护理连续体中的不同挑战，涵盖从照护者支持和多模态数据分析到自动化研究与临床数据解读。所提出的框架由八个专业化、可互操作的智能体组成。这些智能体按功能分类：（1）照护者与患者支持，（2）数据分析与研究，以及（3）高级多模态工作流。该方法论详细说明了每个智能体的技术架构，利用了一系列先进技术，包括大型语言模型（LLM）（如GPT-4o和Gemini）、多智能体编排框架、用于基于证据响应的检索增强生成（RAG），以及用于网络抓取、多模态数据处理和内存数据库查询的专门工具。本文为AD护理的集成AI生态系统提供了详细架构蓝图。通过超越单一用途工具，转向协作式多智能体范式，该框架为开发更具适应性、个性化和主动性的解决方案奠定了基础。这种方法论途径旨在为未来能够综合多样化数据流以改善患者预后并减轻照护者负担的系统铺平道路。
英文摘要：
Alzheimer's disease (AD) presents a complex, multifaceted challenge to patients, caregivers, and the healthcare system, necessitating integrated and dynamic support solutions. While artificial intelligence (AI) offers promising avenues for intervention, current applications are often siloed, addressing singular aspects of the disease such as diagnostics or caregiver support without systemic integration. This paper proposes a novel methodological framework for a comprehensive, multi-agent system (MAS) designed for holistic Alzheimer's disease management. The objective is to detail the architecture of a collaborative ecosystem of specialized AI agents, each engineered to address a distinct challenge in the AD care continuum, from caregiver support and multimodal data analysis to automated research and clinical data interpretation. The proposed framework is composed of eight specialized, interoperable agents. These agents are categorized by function: (1) Caregiver and Patient Support, (2) Data Analysis and Research, and (3) Advanced Multimodal Workflows. The methodology details the technical architecture of each agent, leveraging a suite of advanced technologies including large language models (LLMs) such as GPT-4o and Gemini, multi-agent orchestration frameworks, Retrieval-Augmented Generation (RAG) for evidence-grounded responses, and specialized tools for web scraping, multimodal data processing, and in-memory database querying. This paper presents a detailed architectural blueprint for an integrated AI ecosystem for AD care. By moving beyond single-purpose tools to a collaborative, multi-agent paradigm, this framework establishes a foundation for developing more adaptive, personalized, and proactive solutions. This methodological approach aims to pave the way for future systems capable of synthesizing diverse data streams to improve patient outcomes and reduce caregiver burden.
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