Artificial Intelligence and Alzheimer’s Disease: Bridging Complexity with Precision Medicine

The most prevalent cause of dementia and a progressive neurodegenerative illness, Alzheimer's disease (AD) has a substantial negative impact on both global health and the economy. There is presently no cure, despite much study, and treatments like memantine and cholinesterase inhibitors just alleviate symptoms. The multifaceted character of AD, comprising intricate genetic, epigenetic, and environmental connections, has been brought to light by developments in genomics, neuroimaging, and clinical data. Novel computational techniques are necessary since traditional methods often fail to understand such high-dimensional information. In AD research, artificial intelligence (AI), especially machine learning and deep learning, has become a game-changing tool. In order to enable early diagnosis, prognosis, biomarker identification, and therapy development, artificial intelligence (AI) makes it easier to analyze large datasets from next-generation sequencing (NGS), transcriptomics, proteomics, imaging, and genome-wide association studies (GWAS). AI applications in AD include determining transcriptomic and epigenetic biomarkers, discovering new gene-gene interactions, connecting neuroimaging indicators with genetic differences, and predicting disease risk using genetic risk scores. Furthermore, by combining multifaceted biological and clinical data, AI-driven methods facilitate drug discovery, repurposing, and clinical trial optimization. Recent research highlights AI's promise in precision medicine for AD by showing that it can combine genetic, imaging, and biomarker data to reach high prediction accuracy. Nonetheless, there are still issues with clinical validation, data heterogeneity, and interpretability. The uses of AI in deciphering the genetics and pathophysiology of AD are highlighted in this study, along with current advancements and constraints. It also offers insights into potential future paths where AI might speed up the conversion of complicated data into useful methods for AD diagnosis and therapy.