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 …

Introduction Artificial intelligence through machine learning uses algorithms and prior
learnings to make predictions. Recently, there has been interest to include more artificial …

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review,
written by Benjamin Kompa, Joe B. Hakim, Anil Palepu, Kathryn Grace Kompa, Michael …

Artificial intelligence (AI), a highly interdisciplinary science, is an increasing presence in
pharmacovigilance (PV). A better understanding of the scope of artificial intelligence in …

Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the
earliest by processing suspected adverse event (AE) reports and extraction of health data …

Artificial intelligence, especially machine learning models (herein also referred to as
“machines”), increasingly make important automated decisions (eg, classifying, predicting …

Artificial intelligence (AI) holds the promise of boosting efficiency of processes and
increasing insights into data across all aspects of life, including healthcare, such as for …

Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …

Abstract Background and Objective. Explainable Artificial Intelligence (XAI) has been
identified as a viable method for determining the importance of features when making …

There is great interest in the application of 'artificial intelligence'(AI) to pharmacovigilance
(PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application …