Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

Abstract

Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this study is to identify studies using XAI in the field of pharmacovigilance. Only recently, there have been many attempts, so few papers were actually selected, but a total of 781 papers were confirmed, and 25 of them manually met the selection criteria. In this paper, we present an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, we identify key challenges for several research issues of XAI in pharmacovigilance. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not. Therefore, the challenges involved and future prospects should be continuously discussed. Author