Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection is crucial for effective intervention and improved patient outcomes. This study explores the integration of Artificial Intelligence (AI) with Magnetic Resonance Imaging (MRI) and Natural Language Processing (NLP) for early AD detection. Deep learning models such as Convolutional Neural Networks (CNNs) analyze MRI scans to detect neurodegenerative patterns, while NLP techniques process cognitive assessment data to identify linguistic biomarkers associated with AD. Additionally, Database Management Systems (DBMS) play a crucial role in storing and managing medical data efficiently, while Data Warehouses aggregate multi-source health recordsto enable large-scale analysis. Data Mining techniques are employed to extract hidden patterns from patient data, further enhancing the predictive accuracy of AI models. The fusion of these modalities enhances diagnostic accuracy. This research highlights the potential of AI driven techniques in medical diagnostics and suggests future improvements for realworld implementation.
Alzheimer’s Disease, Artificial Intelligence, Deep Learning, MRI, Natural Language Processing, Early Detection, Cognitive Assessment, DBMS, Data Warehouse, Data Mining.