Pharmacovigilance (PV) is essential for monitoring the safety and efficacy of medications, ensuring public health protection. With the advent of precision medicine, which tailors treatments to individual patient characteristics, the complexity of PV has increased, necessitating advanced methodologies. Harnessing big data and artificial intelligence (AI) offers transformative potential for enhancing PV in precision medicine. Big data, encompassing diverse sources such as electronic health records (EHRs), genomic data, and social media, provides a comprehensive view of drug effects across varied populations. AI techniques, including machine learning (ML) and natural language processing (NLP), enable the efficient processing and analysis of this vast and complex data, facilitating early detection of adverse drug reactions (ADRs), prediction of drug interactions, and personalized risk assessment. This review explores the current state and potential of big data and AI in PV, highlighting their applications, benefits, and challenges. Key topics include the integration of heterogeneous data sources, development of predictive models, real-time monitoring, and ethical considerations. The review concludes with recommendations for future research and practice to fully leverage these technologies in achieving safer and more effective pharmacotherapy in the era of precision medicine.
Pharmacovigilance (PV), Precision Medicine, Big Data, Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Electronic Health Records (EHRs)
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