## 8 Discussion and Conclusions  
Alzheimer's disease (AD) remains one of the most complex and difficult neurodegenerative disorders to diagnose, especially during its early stages when intervention can be most impactful. Accurate and timely detection is crucial, as early diagnosis allows clinicians to initiate therapeutic strategies that can slow disease progression, preserve cognitive function, and improve patients' quality of life. This review underscores the emerging potential of generative artificial intelligence (AI), particularly generative models such as Generative Adversarial Networks (GANs) and Diffusion Models as a powerful complement to conventional neuroimaging analysis in the quest for early-stage detection. By synthesizing high-quality brain images, these models not only help overcome the challenge of limited datasets but also enhance feature extraction capabilities. This enables downstream classifiers to detect subtler neurodegenerative patterns that might be overlooked by traditional techniques. One major challenge in Alzheimer's diagnosis is the lack of high-quality, labeled neuroimaging data. Generative models help overcome this by producing synthetic MRI and PET scans that closely mimic real ones, enhancing data diversity and improving diagnostic accuracy. Some studies even reported up to 99.7% accuracy with GANs and high-quality metrics from Diffusion Models (e.g., SSIM > 0.92, PSNR > 30 dB). Unlike traditional reviews that just list ML models, this paper focuses on how generative approaches like DCGAN, CycleGAN, WGAN, and conditional DDPMs actively enhance early detection by augmenting data, capturing disease patterns, and modeling progression. Generative models stand out for their ability to integrate diverse data types like MRI, PET scans, and CSF biomarkers, offering a fuller view of Alzheimer's progression. When combined with CNNs, these models do not just aid diagnosis they elevate it. But there is a trade-off to consider. While these models often deliver excellent results, they can be hard to  
Figure 11: Key differences between GAN and Diffusion Models. interpret due to a lack of transparency. In clinical settings, trust matters, and clinicians need to understand how a model reaches its decision. Unfortunately, most GANs and diffusion models still operate like black boxes. Coupled with their high computational demands, these limitations currently hinder their widespread adoption in clinical practice. Many DCGAN-based studies (e.g., [26; 28]) generate 2D slices only, losing volumetric context and cross-slice biomarkers. Implementing 3D GAN architectures (e.g., 3D-CycleGAN [23], 3D-BicycleGAN [36]) or spatio-temporal diffusion models are recommended to preserve full brain topology. Even though the research landscape is promising, not all findings are consistent. Models trained on datasets like ADNI often struggle when tested on others like OASIS, raising concerns about generalizability. GAN classifiers fine-tuned on ADNI drop 10-15% accuracy on OASIS or AIBL, likely due to scanner/vendor differences. Domain adaptation methods, such as adversarial feature alignment or style-transfer approaches (e.g., Cycle GAN-based harmonization), along with fine-tuning using small subsets from target datasets, can help overcome these domain discrepancies. In some cases, adding synthetic images did not improve accuracy much, especially when the generated data lacked diversity or detail. DCGAN outputs often collapse to a few modes, leading to overfitting when used for augmentation [26; 28]. Strategies such as using WGAN-GP or Spectral Normalization to stabilize training, enforce latent-space regularization, or switch to diffusion models which naturally sample diverse modes could mitigate these problems. Another challenge is validation. Metrics like SSIM and PSNR measure image quality, but they do not guarantee medical accuracy. Many studies still lack clinical validation through expert reviews or pathology comparisons, making it hard to assess their real-world utility.