Darmendra Ramcharran, Jeffery L. Painter, Vijay Kara, Michael Gläser, Marco Vanini, Venkateswara Rao Chalamalasetti, Christopher Golds, Aya Abdelkarim, Andrew Bate, Jens‐Ulrich Stegmann
This study highlights the potential of ML-based tools to improve pharmacovigilance by enhancing signal detection performance, reducing the likelihood of missed signals, while increasing operational efficiency, and strengthening reproducibility and transparency. While MLIT demonstrated high concordance with expert decisions and provided meaningful time savings, human oversight remains essential, especially for low-confidence predictions. Ongoing refinement and user engagement will be critical for broader implementation and further automation, marking a significant step forward in ensuring safer and more efficient drug safety surveillance.
The advent of generative artificial intelligence (GenAI) has introduced both remarkable opportunities and significant challenges in the field of pharmacovigilance (PV). This perspective review reflects on emerging trends, practical use cases, and conceptual frameworks shaping the integration of GenAI in high-risk domains such as drug and vaccine safety monitoring. We draw on current experiments and early real-world applications to examine the potential benefits, inherent risks, and propose a framework for integrating GenAI into PV systems, emphasizing the necessity of rigorous testing, human oversight, and ethical considerations. Our goal is to support PV professionals and stakeholders in navigating this rapidly evolving landscape by identifying promising strategies and implementation pathways.