AgenticAD: A Specialized Multiagent System Framework for Holistic Alzheimer Disease Management
Adib Bazgir, Amir Habibdoust, Xing Song, Yuwen Zhang
TL;DR
Alzheimer's care is a multifaceted, data-intensive challenge that is hampered by siloed AI tools. The paper introduces AgenticAD, an eight-agent multi-agent system framework that unifies caregiver support, data analysis, and multimodal workflows using technologies such as large language models, Retrieval-Augmented Generation, and specialized tools for web scraping, data processing, and imaging analysis. It demonstrates end-to-end capabilities across eight specialized roles, including a coordinated care-planning swarm, a RAG-backed PDF assistant, automated deep-research, structured data analytics, and multimodal imaging education, while openly addressing failure modes and integration needs. The work argues that a tightly integrated MAS can synthesize heterogeneous data streams to improve patient outcomes and reduce caregiver burden, but requires secure interoperability, clinical validation, and robust orchestration to translate this paradigm into real-world dementia care.
Abstract
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.