A neurological condition that worsens over time, Alzheimer’s disease (AD) is typified by memory loss, cognitive decline, and functional degradation. Traditional diagnostic techniques such as neuroimaging, cerebrospinal fluid biomarkers, and neuropsychological testing are often intrusive, costly, or insensitive in the early stages. Recent years have seen the emergence of AI and ML as game-changing technologies for AD risk assessment, early detection, and customized prevention. Using sophisticated models such as deep learning, convolutional neural networks (CNNs), and graph-based algorithms, AI-driven methods achieve high performance: CNNs, for example, have reached diagnostic accuracies of 94–99% for early AD and mild cognitive impairment using multimodal MRI and PET data. However, most reported performance metrics are derived from retrospective analyses and internal validation cohorts, with limited external validation across diverse populations. These methods include multimodal data integration from neuroimaging, genetics, and clinical records. Years before symptoms appear, AI-based frameworks can predict disease progression, identify modifiable risk factors, and guide individualized treatment plans. Future developments in federated learning and explainable AI (XAI) are promising, although data privacy, algorithmic bias, and ethical ramifications are concerns. Overall, AI and ML have a great deal of promise to transform the prevention of AD, enabling precision therapy and enhancing the lives of those who are at risk.
Singh et al. (Tue,) studied this question.