Artificial intelligence and multi-omics integration advances precision diagnosis and personalized therapy for Alzheimer’s disease

Alzheimer’s disease (AD) is a multifaceted neurodegenerative disorder that complicates standard diagnostic and therapeutic approaches due to its molecular complexity and clinical variability. Integrating artificial intelligence (AI) with multi-omics technologies such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics establishes an innovative approach to precision medicine in AD. Multi-omics provides a comprehensive perspective on disease pathophysiology, whereas AI facilitates scalable, high-dimensional analysis, improving early diagnosis, subtype classification, and targeted therapy. AI-driven models are proficient at recognizing clinically significant trends, detecting new biomarkers, predicting cognitive decline, and exploring alternative uses for drugs. This review examines the present state of omics-AI integration in AD, analyzes case studies showcasing practical applications, and addresses federated learning, real-world data, and digital biomarkers as novel instruments for clinical translation. Despite substantial progress, challenges remain, including data heterogeneity, model interpretability, and ethical issues related to algorithmic bias and data privacy. To optimize AI-integrated multi-omics pipelines, interdisciplinary collaboration is essential. This collaboration will transform our approach to identifying and treating AD, leading to more personalized and proactive treatment.