Pharmacovigilance in the Era of Artificial Intelligence: Advancements, Challenges, and Considerations

Pharmacovigilance (PV) is a science that plays a crucial role in protecting patients by detecting adverse drug reactions (ADRs). PV can do this by collecting and analyzing data from a wide variety of healthcare sources. However, traditional PV methods face limitations, particularly in accurately and efficiently analyzing large datasets. This limitation leads to underreported ADRs, which negatively impact many patients. However, with the recent rise in artificial intelligence, PV as a science has the potential to improve. This can be done by incorporating different subsets of AI, such as machine learning (ML) and natural language processing (NLP), into PV. The aim of this study is to describe how integrating AI, specifically ML and NLP, into PV systems can improve data collection, data processing, and the detection of ADRs. A comprehensive literature search was conducted using PubMed and Google Scholar to examine studies that were conducted within the last 30 years. Twenty-eight studies were included in this paper. Inclusion criteria included articles that were written in English, articles focusing on PV as a science, ADRs, AI's current role in PV, and AI's potential role in PV. Exclusion criteria included studies that were not published in English and studies that were published more than 30 years ago. The findings from several systematic reviews that explore the implementation of AI into PV indicate that AI can improve PV by enhancing the efficiency and accuracy of detecting ADRs. Through ML algorithms, ADRs can be identified more quickly and accurately compared to traditional PV methods; while using the NLP model, AI is able to extract relevant patient data from unstructured data sources such as electronic health records (EHRs) and report certain drug interactions more accurately and efficiently. However, there are limitations to incorporating AI into PV. These include ethical, legal, and privacy concerns; interpretative limitations if certain datasets are incomplete and are missing information; the lack of current research; and the need to conduct more research on this topic to definitively determine whether AI should be incorporated into PV. With the exponential development of technology such as AI, there is a lot of promise in strengthening PV into a more accurate and efficient ADR detection system. While there is some research highlighting AI's potential to enhance PV, much more research needs to be conducted to fully substantiate this claim. Incorporating AI into PV does, however, have the potential to change ADR detection methods for the better.