Alzheimer's disease remains a complex condition with an unclear cause and no known cure.
Current treatments focus on symptom management and slowing disease progression. Research is ongoing
to uncover its underlying mechanisms, develop effective treatments, and explore early detection and
prevention strategies.
Genetic data plays a crucial role in Alzheimer's detection, offering significant advantages. Genome-wide
association studies (GWAS) have identified numerous genetic variants linked to the disease. Large-scale
genetic analyses help researchers understand disease pathways, identify potential drug targets, and
contribute to novel therapeutic developments.
This review aims to highlight research gaps and limitations while proposing future directions for
advancing the field. It provides a detailed survey outlining essential criteria for improving genetic-based
detection methods. Researchers can enhance accuracy by selecting optimal approaches for genetic
analysis. The review focuses on recent studies that integrate genetic data with artificial intelligence (AI) to
identify mutated genes associated with Alzheimer's and classify the disease efficiently.
Findings indicate that, despite a relatively small body of published research, studies in this field have grown
exponentially since 2020. This review offers a comprehensive analysis of genetic and AI-driven approaches
for Alzheimer's detection. It serves as a valuable resource for researchers, clinicians, and policymakers,
shedding light on the current state of the field, guiding future research, and supporting the development of
more accurate and effective early detection methods for Alzheimer's disease.