Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

Introduction: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is estimated that more than 90% of AEs go unreported. In this landscape, embracing assistive technologies at scale becomes necessary to obtain a higher yield of AEs, to maintain compliance, and transform the PV professional work life.

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.

Introduction: Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. Adverse event seriousness is a key factor in defining reporting timelines and is often performed manually by pharmacovigilance experts. The dramatic increase in the volume of safety reports necessitates exploration of scalable solutions that also meet reporting timeline requirements.

Training Augmented Intelligent Capabilities for Pharmacovigilance: Applying Deep-learning Approaches to Individual Case Safety Report Processing.

Introduction: Regulations are increasing the scope of activities that fall under the remit of drug safety. Currently, individual case safety report (ICSR) collection and collation is done manually, requiring pharmacovigilance professionals to perform many transactional activities before data are available for assessment and aggregated analyses. For a biopharmaceutical company to meet its responsibilities to patients and regulatory bodies regarding the safe use and distribution of its products, improved business processes must be implemented to drive the industry forward in the best interest of patients globally.

Artificial Intelligence and the Future of the Drug Safety Professional.

The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from individual case safety reports (ICSRs). To cope with this increase, more healthcare and qualified professionals are required to capture and evaluate the data. To address the evolving landscape, it will be necessary to embrace assistive technologies such as artificial intelligence (AI) at scale.