Artificial Intelligence and Machine Learning in Pharmacovigilance: Transforming Adverse Drug Reaction Detection and Signal Management.
Year: 2026
Type: article
Abstract: The science and collection of practices known as pharmacovigilance focus on the identification, evaluation, comprehension, and avoidance of side effects and other issues associated to medications. Conventional pharmacovigilance mostly relies on literature, clinical records, spontaneous reports, and organized safety databases. These sources include useful information but are impacted by under-reporting, diverse terminology, insufficient case information, reporting delays, duplicate records, and the huge volume of data requiring human assessment. As tools to assist these operations, artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and similar computational techniques are being studied more and more. Pharmacovigilance (PV) experimental uses of artificial intelligence (AI) have quickly progressed to routine usage. The promise of AI to transform drug safety monitoring is critically examined in this study, which focuses on practical implementation concerns including assuring AI's consistent and transparent performance, eliminating many sources of bias, and resolving interpretability issues. It highlights the shift of PV from experimental usage to regular, scalable capabilities. It looks at the evidentiary foundation for AI in particular applications, how it might improve actionable insights, and how businesses can protect themselves from unforeseen effects in multi-AI system setups. Current legislative and international initiatives increasingly stress responsible AI, human supervision, transparency and lifecycle governance. Therefore, rather than taking the role of pharmacovigilance experts, AI should be largely seen as an enhancement technology. Properly verified technologies may decrease repetitious burden and allow professionals focus on clinically meaningful evaluation, although final safety judgments should remain subject to appropriate expert and regulatory review.
Language: English
Subfield: Toxicology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225781385
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