RECENT ADVANCES IN THE PHARMACOLOGICAL MANAGEMENT OF ALZHEIMER’S DISEASE: INTEGRATING EMERGING THERAPIES WITH AI AND COMPUTATIONAL APPROACHES FOR PRECISION MEDICINE

A major worldwide neurodegenerative disease Alzheimer's disease (AD) is distinguished by its intricate pathophysiology and poor response to treatment.The effectiveness of conventional biomarkers and medication targets (A, tau) as well as symptomatic therapies (cholinesterase inhibitors memantine) has been modest.This paper examines the ways in which artificial intelligence specifically machine learning and deep learning has transformed drug development, biomarker identification and AD diagnosis.AI has made it possible for early detection, risk stratification and precision therapy by combining multi-modal data (neuroimaging, genomes, proteomics and electronic health records).We showcase the most advanced techniques including CNNs, RNNs, Transformers, GNNs and knowledge graphs and we talk about how AI-driven drug screening and repurposing pipelines (like sildenafil for possible AD therapy) speed up the discovery process.In order to improve precision care in AD we also look at methodological issues, gaps in clinical validation and suggest an integrated AI framework.