多模态人工智能在阿尔茨海默病早期诊断中的应用：现状、挑战与临床转化路径

doi:10.3969/j.issn.1006-5725.2026.09.012

Abstract

Abstract:

Alzheimer's disease （AD） is a progressive neurodegenerative disorder with currently limited therapeutic options. Early diagnosis is pivotal for delaying disease progression； however， traditional diagnostic methods remain constrained by significant subjectivity and insufficient accuracy， particularly in early-stage or atypical cases. Against this backdrop， this review explores the applications， challenges， and clinical translation pathways of multimodal artificial intelligence （AI） in the early diagnosis of AD. By integrating multidimensional data-including neuroimaging， biomarkers， and clinical assessments-multimodal AI can uncover latent disease patterns， offering a novel， highly sensitive， and objective tool for the early identification and differential diagnosis of AD. Nevertheless， several barriers hinder its clinical translation， including the complexity of AD pathophysiology， data heterogeneity， and the lack of model interpretability. Future efforts must prioritize multidisciplinary collaboration， advance research into explainable AI， and facilitate seamless integration into clinical workflows. Ultimately， these strides will pave the way for precision diagnosis and personalized treatment of AD.

Key words: Alzheimer's disease, early diagnosis, multimodal artificial intelligence, machine learning, deep learning, clinical translation

CLC Number:

R741.04

Xiaorui QIU,Yidan ZHANG,Guofeng YANG. Applications of multimodal artificial intelligence in early Alzheimer's diagnosis： A review of current trends， obstacles， and clinical translational strategies[J]. The Journal of Practical Medicine, 2026, 42(9): 1579-1585.

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Applications of multimodal artificial intelligence in early Alzheimer's diagnosis： A review of current trends， obstacles， and clinical translational strategies

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