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Mar 17, 2026
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Abstract

Artificial intelligence (AI) is reshaping pharmacovigilance (PV) by improving the detection, evaluation, and prevention of adverse drug reactions. Traditional PV processes are limited by manual case handling and delayed signal detection, whereas modern AI methods machine learning, deep learning, and natural language processing enable rapid analysis of large, complex, and unstructured real-world data. These technologies enhance automation of case processing, improve extraction of safety information from electronic health records and social media, and uncover subtle patterns that support earlier and more accurate signal detection. Despite these advances, challenges remain related to data quality, model transparency, regulatory expectations, and integration into existing PV workflows. This review summarizes recent progress, applications, and future directions of AI in pharmacovigilance, emphasizing its growing role in automation, deep learning, signal detection, and real-world data analytics.

Keywords

Pharmacovigilance, Signal Detection, Adverse Drug Reactions (ADRs), Automation.

Article Details

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