Automation in pharmacovigilance: artificial intelligence and machine learning for patient safety

Abstract

Automation promises to be a game- change for pharmacovigilance decreasing the cost of case reporting and improving data quality to truly add value, including signal detection in drug safety. Pharmacovigilance analytic and benefit – risk assessment.Technology advances are playing a major role in pharmaceutical PV strategy updates. For example more companies are looking towards cloud- based solutions, mobile applications, robotic automation, artificial intelligence and big data analytics as a vital part of clinical safety and regulatory operations in the pharmaceutical industry. Applying innovative technology automation tools and processes to PV strategies is now a critical requirement for managing the safety of pharmaceutical products. The role of artificial intelligence and machine learning in pharmacovigilance can enhance the productivity in identification, detection, management and reporting of ADRs. The main objective ofArtificial intelligence is meant to challenges to implementing intelligent automatic solutions include finding / having appropriate training data for machine learning models and the need for harmonised regulatory guidance. AI can analyse and interpret data at lightning speed, never gets tried or sick and can simply work by 24/7. Thousands of adverse effects are processed every month by ICSR in PV that incudes native automation and standalone technologies like AI and ML that reduce the manual effort.