AD-GPT: Large Language Models in Alzheimer's Disease
Journal:
arXiv
Published Date:
Apr 3, 2025
Abstract
Large language models (LLMs) have emerged as powerful tools for medical
information retrieval, yet their accuracy and depth remain limited in
specialized domains such as Alzheimer's disease (AD), a growing global health
challenge. To address this gap, we introduce AD-GPT, a domain-specific
generative pre-trained transformer designed to enhance the retrieval and
analysis of AD-related genetic and neurobiological information. AD-GPT
integrates diverse biomedical data sources, including potential AD-associated
genes, molecular genetic information, and key gene variants linked to brain
regions. We develop a stacked LLM architecture combining Llama3 and BERT,
optimized for four critical tasks in AD research: (1) genetic information
retrieval, (2) gene-brain region relationship assessment, (3) gene-AD
relationship analysis, and (4) brain region-AD relationship mapping.
Comparative evaluations against state-of-the-art LLMs demonstrate AD-GPT's
superior precision and reliability across these tasks, underscoring its
potential as a robust and specialized AI tool for advancing AD research and
biomarker discovery.
