BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring
Rajan Das Gupta, Md Kishor Morol, Nafiz Fahad, Md Tanzib Hosain, Sumaya Binte Zilani Choya, Md Jakir Hossen
2025-11-05
Summary
This paper introduces BRAINS, a new system designed to help detect Alzheimer's disease, especially in places where it's hard to get expensive and complex medical tests.
What's the problem?
Alzheimer's disease is becoming more common globally, and finding it early is really important for potential treatments and managing the disease, but many areas lack the resources for thorough and expensive diagnostic testing.
What's the solution?
The researchers created BRAINS, which uses the power of artificial intelligence, specifically large language models, to analyze patient data. It works in two parts: one part assesses risk based on things like memory test scores and brain scans, and the other finds similar patient cases to help provide context and improve accuracy. These two parts work together to help determine how severe the disease is or if someone is showing early signs of cognitive decline.
Why it matters?
BRAINS has the potential to make Alzheimer's detection more accessible and scalable, meaning more people could be screened, even in areas with limited resources. It also aims to be explainable, so doctors can understand *why* the system made a certain prediction, and could help identify the disease at earlier stages when interventions might be more effective.
Abstract
As the global burden of Alzheimer's disease (AD) continues to grow, early and accurate detection has become increasingly critical, especially in regions with limited access to advanced diagnostic tools. We propose BRAINS (Biomedical Retrieval-Augmented Intelligence for Neurodegeneration Screening) to address this challenge. This novel system harnesses the powerful reasoning capabilities of Large Language Models (LLMs) for Alzheimer's detection and monitoring. BRAINS features a dual-module architecture: a cognitive diagnostic module and a case-retrieval module. The Diagnostic Module utilizes LLMs fine-tuned on cognitive and neuroimaging datasets -- including MMSE, CDR scores, and brain volume metrics -- to perform structured assessments of Alzheimer's risk. Meanwhile, the Case Retrieval Module encodes patient profiles into latent representations and retrieves similar cases from a curated knowledge base. These auxiliary cases are fused with the input profile via a Case Fusion Layer to enhance contextual understanding. The combined representation is then processed with clinical prompts for inference. Evaluations on real-world datasets demonstrate BRAINS effectiveness in classifying disease severity and identifying early signs of cognitive decline. This system not only shows strong potential as an assistive tool for scalable, explainable, and early-stage Alzheimer's disease detection, but also offers hope for future applications in the field.