Artificial Intelligence and Machine Learning for Drug Safety

Abstract

This chapter explores the history, rationality, important, applications, advantages, disadvantages, and best practices of using artificial intelligence (AI) and machine learning in drug safety. AI and machine learning can be used to improve drug safety by identifying adverse drug events, predicting drug–drug interactions, and identifying patient populations at risk for adverse events. The advantages of using AI and machine learning for drug safety include increased accuracy, efficiency, and speed in identifying adverse events. However, there are also potential disadvantages and ethical concerns that must be addressed, such as the risk of bias and the need to ensure patient privacy and security. To ensure the effective and ethical use of AI and machine learning in drug safety, it is important to follow best practices such as using reliable data sources, using validated algorithms, ensuring scalability, and addressing ethical concerns. By following the best practices, it is possible to develop and implement AI and machine learning-based tools for drug safety that improve patient outcomes and advance the field of pharmacovigilance.