# ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator

arXiv: https://arxiv.org/abs/2506.11150 · HTML: https://mlsanity.com/paper/2506.11150/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [MMedAgent: Learning to Use Medical Tools with Multi-modal Agent](https://mlsanity.com/paper/2407.02483/) (2024)
- [MedRAX: Medical Reasoning Agent for Chest X-ray](https://mlsanity.com/paper/2502.02673/) (2025)
- [nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model](https://mlsanity.com/paper/2402.03526/) (2024)
- [An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI](https://mlsanity.com/paper/2008.04024/) (2020)
- [CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification](https://mlsanity.com/paper/2403.16520/) (2024)
- [Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting](https://mlsanity.com/paper/2203.00157/) (2022)
- [HIBMatch: Hypergraph Information Bottleneck for Semi-supervised Alzheimer's Progression](https://mlsanity.com/paper/2303.10390/) (2023)

## Built on by (most cited first, 9 of 9)

- [NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research](https://mlsanity.com/paper/2605.06584/) (2026)
- [AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study](https://mlsanity.com/paper/2603.25322/) (2026)
- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026)
- [What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection](https://mlsanity.com/paper/2602.11177/) (2026)
- [Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity](https://mlsanity.com/paper/2603.17392/) (2026)
- [PRA-PoE: Robust Multimodal Alzheimer's Diagnosis with Arbitrary Missing Modalities](https://mlsanity.com/paper/2605.13081/) (2026)
- [Dementia-Agents: A Multi-Modal Multi-Agent System for Dementia Staging and Phenotyping](https://mlsanity.com/paper/2606.21168/) (2026)
- [iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis](https://mlsanity.com/paper/2607.08778/) (2026)

## Abstract (arXiv)

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Early and precise diagnosis of AD is crucial for timely intervention and treatment planning to alleviate the progressive neurodegeneration. However, most existing methods rely on single-modality data, which contrasts with the multifaceted approach used by medical experts. While some deep learning approaches process multi-modal data, they are limited to specific tasks with a small set of input modalities and cannot handle arbitrary combinations. This highlights the need for a system that can address diverse AD-related tasks, process multi-modal or missing input, and integrate multiple advanced methods for improved performance. In this paper, we propose ADAgent, the first specialized AI agent for AD analysis, built on a large language model (LLM) to address user queries and support decision-making. ADAgent integrates a reasoning engine, specialized medical tools, and a collaborative outcome coordinator to facilitate multi-modal diagnosis and prognosis tasks in AD. Extensive experiments demonstrate that ADAgent outperforms SOTA methods, achieving significant improvements in accuracy, including a 2.7% increase in multi-modal diagnosis, a 0.7% improvement in multi-modal prognosis, and enhancements in MRI and PET diagnosis tasks.