Artificial Intelligence And Its Integration In Pharmacy

In recent years, the integration of Artificial Intelligence (AI) into various aspects of the pharmaceutical pipeline has emerged as a game-changing trend, revolutionizing the way we approach drug discovery, clinical trials manufacturing, and personalized medicine, and patient care. This review explores AI's role in pharmaceuticals, highlighting its basic concepts, tools, and applications. AI algorithms analyze biological data, predict disease interactions, and optimize drug development processes. Machine learning techniques, such as supervised and unsupervised learning, are employed in drug design, synthesis, and testing. Deep learning models, including convolutional neural networks and recurrent neural networks, are used for image analysis, protein structure prediction, and genomic data analysis. AI integration in pharmacy practice enhances efficiency, reduces errors, and improves patient care. AI assists in disease identification, diagnosis, and personalized treatment plans. Applications include rare disease diagnosis, gastrointestinal inflammation detection, and oncology diagnostics. This review demonstrates AI's potential to transform the pharmaceutical industry, accelerating innovation, improving efficiency, and enhancing patient outcomes.