Harnessing Advanced AI Technologies to Enhance the Diagnosis of Alzheimer’s Disease

The chronic evolving neurodegenerative disorder Alzheimer’s Disease (AD) presents with memory deficits, cognitive impairment, and loss of abilities. AD prevalence is increasing as the world ages, necessitating more precise and easily obtainable diagnostic and treatment approaches. Artificial Intelligence (AI) technologies, and more specifically, machine learning and deep learning, have become game-changers in Alzheimer’s disease patient care, including optimizing care, enabling early diagnosis, supporting differential diagnosis, and predicting disease progression over the last few years. To detect amyloid plaques and hippocampal atrophy, this study investigates how AI-driven imaging analysis, specifically convolutional neural networks applied to MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) scans, provides higher sensitivity and specificity. Artificial intelligence models are also being used to analyze clinical and genomic data to identify biomarkers and support risk stratification. AI-assisted cognitive tests provide scalable, noninvasive, and real-time screening. Telemedicine platforms and AI-based Clinical Decision Support Systems (CDSS) are also improving patient management, particularly in remote or underserved areas. Heterogeneity of data, model explainability, ethics, and regulatory guideline requirements remain issues, despite these latest developments. Beyond recent developments such as federated learning and digital twins, the study comprehensively reviews AI’s contributions to AD diagnosis and therapy. It also establishes a guide for future research directions for the ethical and equitable integration of AI in clinical practice.