BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring

2025-11-04

AI Summary

The escalating global burden of Alzheimer’s disease (AD), particularly in resource-limited settings lacking accessible early diagnostic tools, underscores an urgent need for scalable, interpretable screening solutions. Method: We propose an explainable AI–based AD screening framework tailored for primary care. It features a dual-module architecture—cognitive diagnosis and similar-case retrieval—with a novel case-fusion layer to enhance contextual reasoning. A fine-tuned large language model unifies multimodal inputs (e.g., MMSE, CDR, structural MRI), while patient representation encoding, semantic retrieval, and clinical prompt engineering jointly enable dynamic staging and mild cognitive impairment (MCI) detection. Contribution/Results: Evaluated on real-world clinical data, our method significantly improves accuracy in disease severity classification and provides clinically meaningful, human-interpretable rationales. Designed for low-resource deployment, it requires minimal computational overhead and offers high transparency—establishing a reliable, deployable paradigm for early AD monitoring in underserved settings.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• Detecting Alzheimer's disease early in regions with limited diagnostic tools
• Enhancing Alzheimer's assessment using cognitive and neuroimaging data with LLMs
• Classifying disease severity and identifying early cognitive decline signs

Innovation

• Retrieval-augmented system using LLMs for Alzheimer's detection
• Dual-module architecture with diagnostic and case retrieval components
• Case fusion layer enhances contextual understanding for inference