Application of large language models in early screening of Alzheimer’s disease: a scoping review
- 1School of Nursing, Changchun University of Chinese Medicine, Changchun, China
- 2The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China
Abstract
Objective: To systematically map the current applications of large language models (LLMs) in the early screening of Alzheimer’s disease (AD) and to provide evidence to inform future research and nursing practice.
Methods: This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodological framework. A comprehensive search was performed in PubMed, Web of Science, Embase, Cochrane Library, ProQuest, and Scopus from database inception to April 20, 2026. Eligible studies were screened, selected, and synthesized according to predefined inclusion criteria.
Results: A total of 25 studies were included. The LLMs applied in these studies mainly consisted of general-purpose generative models, multimodal models, and medical-specific models. Data sources primarily included public databases, along with real-world clinical data and simulated data. LLMs were mainly applied for cognitive impairment identification, data augmentation, automated cognitive assessment, and clinical decision support, with cognitive impairment identification being the predominant application direction. Several studies employed prompting strategies including zero-shot prompting, few-shot prompting, chain-of-thought prompting, and agent-based prompting. Multimodal integration and agent-based collaborative reasoning have emerged as important research directions in recent years.
Conclusion: Large language models provide a novel technological approach for early AD screening and demonstrate potential for supporting cognitive assessment and risk identification. Future research should strengthen real-world studies, multicenter validation, and the development of model safety and ethical governance frameworks to facilitate the standardized application of LLMs in early AD screening.
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BibTeX
@article{tian2026application,
title = {{Application of large language models in early screening of Alzheimer’s disease: a scoping review}},
author = {Jianan Tian and Weidong Huang and Yongmei Wen and Yifan Liang},
journal = {Frontiers in Aging Neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnagi.2026.1906641},
url = {https://doi.org/10.3389/fnagi.2026.1906641},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Application of large language models in early screening of Alzheimer’s disease: a scoping review
AU - Jianan Tian
AU - Weidong Huang
AU - Yongmei Wen
AU - Yifan Liang
PY - 2026
DA - 2026/10/12
JO - Frontiers in Aging Neuroscience
DO - 10.3389/fnagi.2026.1906641
UR - https://doi.org/10.3389/fnagi.2026.1906641
AB - Objective: To systematically map the current applications of large language models (LLMs) in the early screening of Alzheimer’s disease (AD) and to provide evidence to inform future research and nursing practice. Methods: This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodological framework. A comprehensive search was performed in PubMed, Web of Science, Embase, Cochrane Library, ProQuest, and Scopus from database inception to April 20, 2026. Eligible studies were screened, selected, and synthesized according to predefined inclusion criteria. Results: A total of 25 studies were included. The LLMs applied in these studies mainly consisted of general-purpose generative models, multimodal models, and medical-specific models. Data sources primarily included public databases, along with real-world clinical data and simulated data. LLMs were mainly applied for cognitive impairment identification, data augmentation, automated cognitive assessment, and clinical decision support, with cognitive impairment identification being the predominant application direction. Several studies employed prompting strategies including zero-shot prompting, few-shot prompting, chain-of-thought prompting, and agent-based prompting. Multimodal integration and agent-based collaborative reasoning have emerged as important research directions in recent years. Conclusion: Large language models provide a novel technological approach for early AD screening and demonstrate potential for supporting cognitive assessment and risk identification. Future research should strengthen real-world studies, multicenter validation, and the development of model safety and ethical governance frameworks to facilitate the standardized application of LLMs in early AD screening.
SN - 1663-4365
ER -