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.