BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and
  Monitoring
Authors: name: Rajan Das Gupta; name: Md Kishor Morol; name: Nafiz Fahad; name: Md Tanzib Hosain; name: Sumaya Binte Zilani Choya; name: Md Jakir Hossen
Published: 2025-11-04 11:27:03

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.