Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. Artificial intelligence focused on machine learning, and can be used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs), processing safety reports, extraction of drug-drug interactions, identification of populations at high risk for drug toxicity or guidance for personalized care, prediction of side effects, simulation of clinical trials, integration of prediction uncertainties into diagnostic classifiers to increase patient safety, etc. Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source.
Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.