Pharmacovigilance in the Era of Digital Health Leveraging Big Data and Artificial Intelligence for Enhanced Drug Safety Monitoring

Abstract

Pharmacovigilance (PV) is the science of drug safety, now established as a separate discipline driven by modern
technologies such as digital health tools, big data, artificial intelligence (AI), and machine learning (ML). Legacy
PV methods relied on manual reporting and spontaneous submissions of adverse drug reactions (ADRs), but
these were hindered by delays in submissions, signal detection, and data quality issues. Emerging technologies
such as AI, digital health, and big data play a critical role in drug safety and risk mitigation. Digital health
tools, including wearable monitors, patient engagement modules, and electronic health records, generate Real-
World Data that help healthcare professionals track patient reactions to drugs over extended periods, providing
insights into genomics, vitals, and ADRs. Big data allows PV practitioners to handle complex, heterogeneous
datasets, including patient reviews, which are analyzed using natural language processing to extract insights
from social media, reports, and clinical data. ML algorithms automate signal detection, predictive modeling, and
casualty assessment, significantly improving the speed and accuracy of ADR identification. Technologies such
as AI-driven platforms (e.g., World Health Organization VigiBase and Food and Drug Administration Sentinel
initiative) demonstrate how large-scale, real-time data can enhance risk signal identification, while digital health
devices assist in monitoring patient vitals for early risk detection. Despite these advancements, challenges persist,
including ethical concerns, data inoperability, algorithm bias, and regulatory issues. This review underscores
the need for global collaboration, standardized reporting methods, and robust regulatory guidelines to address
these challenges. Emerging technologies like the internet of medical things pave the way for personalized and
ML-driven predictive PV.