综述解读

大语言模型筛早期 AD:范围综述看到的证据边界

来源:Front Aging Neurosci · PMID 42935768 · DOI 10.3389/fnagi.2026.1906641** · 来源:IMA 知识库《文献层·文献监控卡片》2026-10-29 期 · 全文级(file_id dGBunAeRiRlm,下载实测 692,872 B) · 全文已获取并通过归属校验

数据日期 2026-10-29 · 生成时间 2026-10-29 06:03

研究问题

Alzheimer’s disease (AD) is the most common neurodegenerative disorder and a leading cause of cognitive impairment and functional disability among older adults worldwide (Carrillo and Mahinrad, 2026).

方法

[Eligibility criteria] Studies were considered eligible if they met the following criteria: (1) participants included individuals with AD, mild cognitive impairment (MCI), or cognitively healthy controls; (2) LLMs were applied for the early screening, identification, detection, or risk prediction of cognitive impairment.;The included models comprised text-based large language models primarily designed for language processing, multimodal large language models capable of processing multiple information sources such as text, images, and speech, and medical-specific models based on LLM architectures with domain-specific language understanding or generation capabilities; (3) the study was an original research article employing quantitative, qualitative, or mixed-methods designs; and (4) the full text was accessible and provided sufficient methodological and outcome information.;Search terms included “Alzheimer’s disease,” “mild cognitive impairment,” “large language model,” “natural language processing,” “screening,” “detection,” and “prediction.” Terms within the same concept were…

核心结论

The studies were conducted primarily in the United States (n = 9, 36%) (Agbavor and Liang, 2022; Guan et al., 2025; Huang et al., 2025; Kearney et al., 2025; Knox et al., 2025; Leng et al., 2025; Li et al., 2025; Zhan et al., 2025; Zolnour et al., 2025), followed by China (n = 6, 24%) (Han et al., 2025; Chen et al., 2026; Guo et al., 2025; Wang Y. et al., 2025; Zhang et al., 2025, 2026), South Korea (n = 3, 12%) (Bang et al., 2024; Cha et al., 2025; Park and Kim, 2025), Poland (n = 1, 4%) (Chlasta et al., 2025), Japan (n = 1, 4%) (Kiguchi et al., 2025), Singapore (n = 1, 4%) (Balamurali and Chen, 2024), Spain (n = 1, 4%) (Pérez-Elvira et al., 2026), Israel (n = 1, 4%) (Zadok et al., 2026), Italy (n = 1, 4%) (Casu et al., 2026), and Tunisia (n = 1, 4%) (Brahmi et al., 2024).;Among them, 24 were observational studies (96%) (Agbavor and Liang, 2022; Balamurali and Chen, 2024; Bang et al.,… ‖ 结论:This study employed a scoping review methodology to systematically summarize the current applications of LLMs in early AD screening.

局限

Furthermore, substantial heterogeneity existed among the included studies in terms of model types, data sources, study designs, and evaluation metrics, which may limit direct comparisons across studies.;Future research should expand the scope of investigation, conduct more high-quality real-world studies, and integrate systematic review approaches to further strengthen the evidence base in this emerging field.

对本项目的价值

★★★☆☆ 脑老化是最大未满足需求,也是终点最难量化的领域;biomarker 与认知读数选择最敏感。队列:ADNI;最大样本量约 9;设计 系统综述、观察性。

全文来源

PMC JATS 全文(正文 43,080 字符,已过标题/DOI 逐字归属校验)

原文题录

  • 英文标题:Application of large language models in early screening of Alzheimer's disease: a scoping review.
  • 期刊与标识:Front Aging Neurosci · PMID 42935768 · DOI 10.3389/fnagi.2026.1906641**

C 级证据分级:C 级(预印本 / 观点评论,非原始研究)

本文属观点/评论类文献,不是原始研究。其论点依赖于作者对既有证据的梳理与判断,不构成新的实验证据。本站标注 C 级即为此意——阅读时请把它当作「领域内的思考框架」,而非「研究发现」。

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