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Background <p>Alzheimer’s disease (AD) research produces extensive genomic and clinical data, yet general large language models (LLMs) often generate inaccurate or superficial outputs. We introduce AD-GPT, a domain-specific framework for reliable information retrieval and synthesis of AD-related knowledge.</p> Methods <p>We integrated curated genomic resources, including cis-eQTL and sQTL data across 13 brain regions from GTEx, genomic location information from NCBI, and gene function annotations from OMIM, together with approximately 150,000 AD-related publications from NCBI’s PubMed. AD-GPT adopts a retrieval-augmented generation (RAG) workflow with task-specific database partitioning, a BERT-based query router, and fine-tuned Llama models, augmented with router and context verifiers to validate task assignment and evidence relevance, supporting three tasks: genetic information retrieval, association study reasoning, and general AD-related knowledge synthesis.</p> Results <p>AD-GPT consistently outperformed strong baseline LLMs in evidence-grounded evaluation metrics across all tasks, including factual consistency, citation validity, and instruction-level faithfulness. Task-specific retrieval and stacked routing improved evidence grounding and substantially reduced hallucination in complex AD-related queries.</p> Conclusion <p>AD-GPT harmonizes curated genomic databases with biomedical literature, offering a scalable and accurate informatics tool to advance AD research.</p>

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AD-GPT: large language models in Alzheimer’s disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月31日

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  • 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

摘要

Background

Alzheimer’s disease (AD) research produces extensive genomic and clinical data, yet general large language models (LLMs) often generate inaccurate or superficial outputs. We introduce AD-GPT, a domain-specific framework for reliable information retrieval and synthesis of AD-related knowledge.

Methods

We integrated curated genomic resources, including cis-eQTL and sQTL data across 13 brain regions from GTEx, genomic location information from NCBI, and gene function annotations from OMIM, together with approximately 150,000 AD-related publications from NCBI’s PubMed. AD-GPT adopts a retrieval-augmented generation (RAG) workflow with task-specific database partitioning, a BERT-based query router, and fine-tuned Llama models, augmented with router and context verifiers to validate task assignment and evidence relevance, supporting three tasks: genetic information retrieval, association study reasoning, and general AD-related knowledge synthesis.

Results

AD-GPT consistently outperformed strong baseline LLMs in evidence-grounded evaluation metrics across all tasks, including factual consistency, citation validity, and instruction-level faithfulness. Task-specific retrieval and stacked routing improved evidence grounding and substantially reduced hallucination in complex AD-related queries.

Conclusion

AD-GPT harmonizes curated genomic databases with biomedical literature, offering a scalable and accurate informatics tool to advance AD research.