The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature

• Published in: Pharmaceutical medicine 2022-10, Vol.36 (5), p.295-306
• Main Authors: Salas, Maribel, Petracek, Jan, Yalamanchili, Priyanka, Aimer, Omar, Kasthuril, Dinesh, Dhingra, Sameer, Junaid, Toluwalope, Bostic, Tina
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Automation; Biomedical and Life Sciences; Biomedicine; Clinical trials; Drug interactions; Hypothesis testing; Identification; Literature reviews; Machine learning; Patient safety; Pharmaceutical Sciences/Technology; Pharmacology/Toxicology; Pharmacotherapy; Pharmacovigilance; Systematic Review
• ISSN: 1178-2595; 1179-1993
• DOI: 10.1007/s40290-022-00441-z

Introduction 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. Objective The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. Methods 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. Results Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine 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 drug–drug 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 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.