## 1 Introduction
Thus, early and accurate diagnosis is essential not only to initiate timely treatments that may slow the progression, but also to give patients and families the opportunity to prepare, seek support, and improve quality of life. Early detection of Alzheimer's disease is critical for improving patient outcomes and guiding timely interventions. Identifying AD at the preclinical stage allows for lifestyle changes, pharmaceutical trials, and supportive care to be initiated when they are most effective. Moreover, early diagnosis can alleviate uncertainty for patients and caregivers, aid in planning, and reduce healthcare costs associated with late-stage care. Recent research has emphasized the importance of biomarkers for the diagnosis and monitoring of Alzheimer's disease. Several biomarkers have been identified as indicators of AD, such as changes in cerebrospinal fluid (CSF), blood-based biomarkers, and particularly neuroimaging biomarkers. Brain imaging has emerged as a critical tool in Alzheimer's research and clinical diagnosis. Modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and functional MRI (fMRI), and Diffusion Tensor Imaging (DTI) provide critical insights into structural and functional brain changes. Making these widely usable to detect structural and functional abnormalities associated with AD. These imaging techniques provide valuable insights into brain atrophy, amyloid deposition, and neuronal activity patterns, serving as non-invasive tools for early diagnosis [8]. Collectively, these imaging modalities enable early-stage identification of pathological changes, often before overt clinical symptoms emerge. Despite their diagnostic value, neuroimaging data presents significant challenges. High-dimensionality, inter-subject variability, limited availability of labeled data, and the cost of image acquisition limit their broader use in population-scale screening. These challenges underscore the need for intelligent systems capable of extracting complex patterns from high-dimensional imaging data. In recent years, generative artificial intelligence (AI) has shown tremendous promise in tackling these limitations. Unlike traditional discriminative models, generative models are capable of learning the underlying data distribution to generate new, synthetic samples that are statistically similar to the original data. Among the most, Generative Adversarial Networks (GANs) and Diffusion Models have emerged as leading frameworks in medical image analysis. These models are capable of generating high-quality synthetic medical images, which can be used to augment small datasets, improve image resolution, and even translate one imaging modality to another (e.g., MRI to PET). GANs introduced by [PERSON] et al. in 2014 [9], with their adversarial architecture, have been applied to a wide range of AD tasks including neuroimaging data augmentation, generating missing imaging modalities, enhancing image resolution, and modelling disease progression. These applications help to overcome the common issue of data imbalance, enhance resolution, and simulate realistic brain pathologies to improve model generalization [10; 11; 12]. Most recently, Diffusion Models, a newer class of generative models based on a stepwise denoising process that reconstructs clean high-fidelity synthetic image data from random noise [13]. These models offer better training stability and high-quality images which makes them suitable for Alzheimer's disease imaging. The integration of GANs and Diffusion Models into AD diagnostic pipelines is transforming the landscape of early detection. By producing high-quality synthetic neuroimages, enhancing data diversity, and improving model performance, GANs and diffusion models enable more accurate and scalable diagnostic systems. Their ability to learn intricate patterns in imaging data makes them a highly suitable diagnostic framework for Alzheimer's disease research and real-world clinical care.