AD-GPT: Large Language Models in Alzheimer's Disease

• URL: http://arxiv.org/abs/2504.03071v1
• Date: Thu, 03 Apr 2025 22:49:10 GMT
• Title: AD-GPT: Large Language Models in Alzheimer's Disease
• Authors: Ziyu Liu, Lintao Tang, Zeliang Sun, Zhengliang Liu, Yanjun Lyu, Wei Ruan, Yangshuang Xu, Liang Shan, Jiyoon Shin, Xiaohe Chen, Dajiang Zhu, Tianming Liu, Rongjie Liu, Chao Huang,
• Abstract summary: Large language models (LLMs) have emerged as powerful tools for medical information retrieval.<n>But their accuracy and depth remain limited in specialized domains such as Alzheimer's disease (AD)<n>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.
• Score: 22.79214699749541
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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