AD-GPT: Large Language Models in Alzheimer's Disease
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arXiv
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917976504008704 |
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| author | Liu, Ziyu Tang, Lintao Sun, Zeliang Liu, Zhengliang Lyu, Yanjun Ruan, Wei Xu, Yangshuang Shan, Liang Shin, Jiyoon Chen, Xiaohe Zhu, Dajiang Liu, Tianming Liu, Rongjie Huang, Chao |
| author_facet | Liu, Ziyu Tang, Lintao Sun, Zeliang Liu, Zhengliang Lyu, Yanjun Ruan, Wei Xu, Yangshuang Shan, Liang Shin, Jiyoon Chen, Xiaohe Zhu, Dajiang Liu, Tianming Liu, Rongjie Huang, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03071 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AD-GPT: Large Language Models in Alzheimer's Disease Liu, Ziyu Tang, Lintao Sun, Zeliang Liu, Zhengliang Lyu, Yanjun Ruan, Wei Xu, Yangshuang Shan, Liang Shin, Jiyoon Chen, Xiaohe Zhu, Dajiang Liu, Tianming Liu, Rongjie Huang, Chao Computation and Language Artificial Intelligence 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. |
| title | AD-GPT: Large Language Models in Alzheimer's Disease |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.03071 |