阿尔茨海默病（Alzheimer’s disease，AD）是老年人群中最为常见的一种神经退行性疾病，目前全球患者数量已将近5 500万例，且目前还没有根治方法。传统的诊断方式由于过于依赖临床量表与主观判断，导致早期诊断敏感度较低；而近年来深度学习与大语言模型的快速发展为AD的早期筛查、精准诊断、病程监测、药物研发以及个性化干预提供了全新的技术途径。本文系统梳理了当前关于AD发病机制的主要假说，深入剖析了通用大模型（如GPT-4、Llama 3、Gemma等）与多模态模型（Flamingo、CLIP、ImageBind）在AD各环节的应用成果，并在此基础上提出一种专门针对AD全病程管理设计的领域特定大模型框架——AD 研究生成式预训练变换模型（AD Research Generative Pre-trained Transformer，ADR-GPT）。该模型通过融合知识、多任务预训练、深度学习与可解释性模块，在数据隐私可控的前提下可实现更高精度的早期预测与个性化干预，旨在为达到“早诊早治、精准干预”提供当前可以实施的技术方案。

Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly population. Currently, the global number of AD patients has reached nearly 55 million, and there is still no cure for the disease to date. Traditional diagnostic methods rely heavily on clinical scales and subjective judgment, resulting in an early diagnostic sensitivity of less than 70%. However, in recent years, the rapid development of deep learning and large language models has provided brand-new technical approaches for AD’s early screening, accurate diagnosis, disease progression monitoring, drug development, and personalized intervention. This article systematically sorts out the main hypotheses regarding the pathogenesis of AD, and in-depth analyzes the application achievements of general large models (such as GPT-4, Llama 3, Gemma, etc.) and multimodal models (Flamingo, CLIP, ImageBind) in various aspects of AD management. On this basis, it proposes a domain-specific large model framework specifically designed for the full-course management of AD — AD Research Generative Pre-trained Transformer (ADR-GPT). By integrating knowledge, multi-task pre-training, deep learning, and interpretability modules, this framework is committed to achieving more accurate early prediction and personalized intervention under the premise of controllable data privacy. It provides a currently implementable technical solution to achieve the goal of “early diagnosis and treatment, precise intervention” for AD.