Artificial Intelligence in Alzheimer’s Disease: A Review of Early Detection

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. Early and accurate diagnosis is critical to delaying disease progression, alleviating clinical symptoms, and improving the long-term quality of life for the affected patients. The deep integration of artificial intelligence (AI) and medical imaging enables efficient early AD screening, overcoming traditional limitations. This study presents a systematic review of AI-driven applications in the early diagnosis of AD with a dual focus on single-modal and multimodal analytical frameworks, comprehensively analyzing core technical components across existing research including data preprocessing pipelines, mainstream deep learning and machine learning diagnostic models, standard performance evaluation metrics, and widely adopted public research datasets, while further qualitatively comparing the diagnostic efficacy and applicability of diverse methodologies across distinct imaging and non-imaging modalities. In addition, this review systematically delineates and compares the application merits, technical bottlenecks, and clinical suitability of AI-enabled diagnostic methods across diverse modalities, providing robust methodological guidance and clear directional references for future research on the early diagnosis of AD and facilitating the advancement of the field toward higher diagnostic precision, broader population applicability, and tighter integration with real-world clinical practice.