The integration of artificial intelligence (AI) and social science media is revolutionizing pharmacovigilance in the era of personalized medicine. This review article explores the applications of AI in pharmacovigilance, including adverse drug reaction detection, real-time monitoring, and personalized medicine. Additionally, the role of social science media in patient engagement, data collection, and risk communication is examined. The article discusses the benefits and challenges of combining AI and social science media in pharmacovigilance, and highlights future directions for research and development. By leveraging these technologies, pharmacovigilance can be enhanced, and patient safety can be improved in the era of personalized medicine.Artificial intelligence (AI) is revolutionizing pharmacovigilance (PV) by enhancing the detection, assessment, and prevention of adverse drug reactions (ADRs). This review examines how AI technologies – such as machine learning (ML), natural language processing (NLP), and big data analytics – tackle existing challenges in pharmacovigilance (PV), including issues like underreporting, large data volumes, and inefficiencies in data processing. AI improves drug safety by automating data collection, enabling real-time adverse event detection, and predicting potential risks, allowing for proactive risk management. Despite challenges in data quality, model interpretability, and regulatory compliance, AI’s role in PV is advancing rapidly, promising more efficient and accurate drug safety monitoring. A concise summary of the article touches on how artificial intelligence (AI) is transforming pharmacovigilance (PV) by enhancing the detection, analysis, and prediction of drug-related adverse events. This review highlights the advancements AI brings to drug safety, such as enhancing efficiency, minimizing human error, and enabling real-time analysis of massive datasets from diverse sources.
Artificial intelligence (AI), pharmacovigilance (PV), signal detection, predictive analytics, natural language processing (NLP) Drug interaction, Adverse effect
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