NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis
Journal:
arXiv
Published Date:
Mar 1, 2025
Abstract
Alzheimer's disease (AD) diagnosis is complex, requiring the integration of
imaging and clinical data for accurate assessment. While deep learning has
shown promise in brain MRI analysis, it often functions as a black box,
limiting interpretability and lacking mechanisms to effectively integrate
critical clinical data such as biomarkers, medical history, and demographic
information. To bridge this gap, we propose NeuroSymAD, a neuro-symbolic
framework that synergizes neural networks with symbolic reasoning. A neural
network percepts brain MRI scans, while a large language model (LLM) distills
medical rules to guide a symbolic system in reasoning over biomarkers and
medical history. This structured integration enhances both diagnostic accuracy
and explainability. Experiments on the ADNI dataset demonstrate that NeuroSymAD
outperforms state-of-the-art methods by up to 2.91% in accuracy and 3.43% in
F1-score while providing transparent and interpretable diagnosis.
