Keywords
Drug Safety Surveillance
Adverse Event Detection
Artificial Intelligence
How to Cite
Abstract
In the field of pharmacovigilance, the timely detection and monitoring of adverse events (AEs) related to drug therapies are critical for ensuring patient safety and regulatory compliance. Traditional pharmacovigilance methods often rely on spontaneous reporting systems and clinical trials, which may not capture all relevant information. In recent years, there has been a growing interest in leveraging artificial intelligence (AI) technologies to enhance drug safety surveillance and AE detection. AI-driven approaches, such as machine learning and natural language processing (NLP), have shown promise in analyzing large volumes of real-world data sources, including electronic health records (EHRs), social media, and online forums, to identify potential AEs associated with drug therapies. This paper provides an overview of AI-driven drug safety surveillance for pharmacovigilance, discussing key methodologies, challenges, and future directions.
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