Evidence map›Paper›PMID 42226232›Full record

ArticleBMC medical informatics and decision making2026

AD-GPT: large language models in Alzheimer's disease.

Ziyu Liu, Lintao Tang, Zeliang Sun, Zhengliang Liu, Yanjun Lyu, Wei Ruan, Yangshuang Xu, Liang Shan, Jiyoon Shin, Xiaohe Chen and 4 more

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In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Ziyu LiuDepartment of Statistics, University of Georgia, Athens, GA, USA.
Lintao TangDepartment of Statistics, Florida State University, Tallahassee, FL, USA.
Zeliang SunDepartment of Epidemiology & Biostatistics, University of Georgia, Athens, GA, USA.
Zhengliang LiuSchool of Computing, University of Georgia, Athens, GA, USA.
Yanjun LyuDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA.
Wei RuanSchool of Computing, University of Georgia, Athens, GA, USA.
Yangshuang XuDepartment of Statistics, Florida State University, Tallahassee, FL, USA.
Liang ShanDepartment of Statistics, Florida State University, Tallahassee, FL, USA.
Jiyoon ShinDepartment of Statistics, University of Georgia, Athens, GA, USA.
Xiaohe ChenDepartment of Statistics, Florida State University, Tallahassee, FL, USA.
Dajiang ZhuDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA.
Tianming LiuSchool of Computing, University of Georgia, Athens, GA, USA.
Rongjie LiuDepartment of Statistics, University of Georgia, Athens, GA, USA. rjliu@uga.edu.
Chao HuangDepartment of Epidemiology & Biostatistics, University of Georgia, Athens, GA, USA. chaohuang@uga.edu.

Funding

NIA NIH HHS R01-AG075582NIH HHS UL1-TR002378
6 · The paper itself

Abstract

backgroundAlzheimer'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.

methodsWe 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.

resultsAD-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.

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

Indexed as

Alzheimer DiseaseGenomicsInformation Storage and RetrievalLarge Language ModelsGenerative Artificial IntelligenceHumansAlzheimer’s diseaseInformation retrievalLarge language models

Identifiers

PMID42226232
PMCPMC13439758

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.