Artificial Intelligence & Machine Learning in Pharmacy & Technology

Abstract

Artificial intelligence (AI) has quickly grown from being tested in drug safety (pharmacovigilance or PV) to being considered for everyday use. This review looks closely at how AI could improve how we monitor drug safety, but also highlights the real-world challenges of using it. These include making sure AI works reliably and clearly, reducing different types of bias, and making AI's decisions easier to understand. The review focuses on how AI can move from being just an experiment to becoming a useful and scalable tool in daily PV work. It also looks at the current evidence for using AI in specific tasks, how AI can provide better insights, and how to prevent problems when using many AI tools together. These points are important as AI becomes a regular part of drug safety monitoring. Artificial intelligence (AI) and machine learning (ML) are revolutionizing the pharmaceutical industry, particularly in drug development and delivery. These technologies enable precision medicine by analyzing extensive datasets to optimize formulations and predict patient responses. AI-driven models enhance nanoparticle-based drug carriers, improving their stability, bioavailability, and targeting accuracy. ML also facilitates real-time monitoring and adaptive control of drug release, ensuring better therapeutic outcomes. This review explores the integration of AI and ML in drug delivery, highlighting their potential to accelerate development, reduce costs, and advance personalized medicine.