Abstract
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. The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review.Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9,2021 using search terms such as pharmacovigilance, patient safety, artificial intelligence, and machine learning in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine describe the use of artificial intelligence in patient safety and pharmacovigilance in general.learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions(ADRs) (57.6%),processing safety reports (21.2%), extraction of drugdrug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety(1.5%).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.Conclusion 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 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..
Keywords
Benefit, Difference between AI and Generative AI, Methods using Pharmacovigilance
How to Cite:
Mahesh A. Mante , Dr.Sunil S. Jaybhaye , Pooja P. Ambhure , REVIEW ON BENEFITS AND METHODS OF USING ARTIFICIAL INTELLIGENCE IN PHARMACOVIGILANCE , Volume 10 , Issue 11, November 2025, EPRA International Journal of Research & Development (IJRD),