人工智能辅助阿尔茨海默病诊断的研究进展
Research Progress on the Application of Artificial Intelligence in Alzheimer’s Disease Diagnosis
DOI: 10.12677/ns.2026.153069, PDF,    科研立项经费支持
作者: 张 皓：陕西工业职业技术大学人事处，陕西 咸阳；张建安, 谢海蝶, 马瑜洁：西藏民族大学附属医院，陕西 咸阳；郑 瑶：西藏民族大学医学院，陕西 咸阳；王平义*：西藏民族大学附属医院，陕西 咸阳；西藏民族大学医学院，陕西 咸阳；中山大学附属第三医院康复医学科，广东 广州
关键词: 阿尔茨海默病；早期诊断；人工智能；深度学习；多模态；Alzheimer’s Disease； Early Diagnosis； Artificial Intelligence； Deep Learning; Multimodal
摘要: 阿尔茨海默病(AD)是神经退行性疾病，早期诊断对延缓病程具有关键意义，人工智能技术在AD诊断中的应用研究进展迅速。当前研究热点包括基于MRI与PET影像的AI分析、语言数据的机器学习评估以及脑电图信号的智能识别等，相关研究表明这些AI技术有助于提高AD早期诊断的准确性和效率，能够及早识别微弱病理特征。然而，目前仍面临数据获取与标准化不足导致的模型泛化性限制、模型可解释性欠缺影响临床信任等挑战，以及临床转化应用的困难。未来发展趋势侧重于多模态数据融合、算法优化并提高AI辅助诊断工具的临床可用性。
Abstract: Alzheimer’s disease (AD) is a neurodegenerative disease where early diagnosis is crucial for slowing its progression, and the application of artificial intelligence (AI) in AD diagnosis has advanced rapidly in recent years. Current research hotspots include AI-based analysis of MRI and PET scans, machine learning assessment of speech and language patterns, and AI-driven EEG signal analysis. Studies indicate that these AI technologies can improve the accuracy and efficiency of early AD diagnosis by enabling earlier detection of subtle pathological changes. However, challenges remain, such as insufficient data sharing and standardization limiting model generalizability, a lack of model interpretability undermining clinician trust, and difficulties in integrating these tools into clinical practice. Future trends focus on multi-modal data integration and algorithm optimization to develop more efficient, clinically usable AI diagnostic tools.
文章引用：张皓, 张建安, 谢海蝶, 郑瑶, 马瑜洁, 王平义. 人工智能辅助阿尔茨海默病诊断的研究进展[J]. 护理学, 2026, 15(3): 62-71. https://doi.org/10.12677/ns.2026.153069

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