Smart Safety: How AI is Transforming Drug Monitoring

Abstract Ensuring the safety of newly launched drugs remains a major challenge in pharmacology, with adverse events contributing to unnecessary morbidity and mortality. Traditional methods, including clinical trials and post-marketing pharmacovigilance, are not error-proof, often failing to detect rare adverse events or drug-drug interactions. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, driven by advancements in machine learning (ML) and deep learning (DL). In pharmacology, AI facilitates target identification, lead compound screening, AI-Guided Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction, and personalized dosage optimization. AI models predict drug efficacy and safety, detect adverse drug events, and optimize treatment outcomes, thus advancing personalized medicine and precision pharmacotherapy. However, challenges such as data quality, ethical concerns, regulatory compliance, and the need for algorithmic validation hinder its widespread adoption. While AI technologies significantly enhance pharmacovigilance systems, they cannot replace the expert judgment of clinical professionals. A balanced approach, integrating AI with human expertise and robust regulatory frameworks, is essential for achieving safer drug use and improved healthcare outcomes.