Artificial intelligence is fundamentally changing how healthcare systems monitor drug safety. First, large language models enable automated extraction of adverse events from unstructured clinical notes at scale, eliminating the labour-intensive manual processes that previously limited surveillance capacity. Second, these systems provide real-time identification of safety signals that traditional reporting mechanisms frequently miss, accelerating the detection of emerging drug-related risks before they affect larger patient populations. Third, AI-powered pharmacovigilance enhances safety monitoring capacity in resource-constrained healthcare settings, where limited personnel and infrastructure have historically hindered comprehensive post-marketing surveillance. As demonstrated in recent research published in Drug Safety, these technologies achieve high accuracy while substantially reducing the operational burden on healthcare professionals. Healthcare administrators, regulatory bodies, and pharmaceutical companies should consider integration of these tools to strengthen drug safety infrastructure. Read the full article on GMJ Newsroom.
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