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 (AI) and machine learning (ML) represent significant advancements in
computing, building on technologies that humanity has developed over millions of years …

The pharmaceutical industry is rapidly adopting new and evolving applications of artificial
intelligence (AI), and so we concur with the point made by Hines et al.(Nat. Rev. Drug …

Adverse drug reactions continue to be not only one of the most urgent problems in clinical
medicine, but also a social problem. The aim of this study was a bibliometric analysis of the …

In recent years there has been growing interest in the use of machine learning across the
pharmacovigilance lifecycle to enhance safety monitoring of drugs and vaccines. Here we …

To help prevent, detect, and resolve security-related problems within a company, AI must be
deployed. Industry 4.0 hopes to significantly improve information security by bringing forth …

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 …

Multiple components factor into the assessment of combination safety risks when two or
more novel individual products are used in combination in clinical trials. These include, but …

В связи с появлением новых путей получения информации о безопасности
лекарственных средств актуальным становится внедрение современных …

Abstract Introduction and Objective Machine learning (ML) systems are widely used for
automatic entity recognition in pharmacovigilance. Publicly available datasets do not allow …