Abstract

IMPACT OF ARTIFICIAL INTELLIGENCE AND SOCIAL MEDIA IN PHARMACOVIGILANCE

*Vaibhav Vishnoi, Ankush Kumar, Prince Verma, Sameer, Hargovind, Anukool Shukla

Abstract

Artificial intelligence (AI) and social science media integration is transforming pharmacovigilance in the age of customized medication. This review examines the use of AI in pharmacovigilance, such as the identification of adverse drug reactions, real-time tracking as well as customized medical care. Furthermore, the function of Social science media in risk assessment, data gathering, and patient involvement We look at communication. The article talks about the advantages and difficulties in integrating social science media with AI in pharmacovigilance, and outlines potential avenues for further study and growth. Using these tools, pharmacovigilance can be improved, and in the age of customized medical care. By improving the identification, evaluation, and avoidance of adverse drug reactions (ADRs), artificial intelligence (AI) is transforming pharmacovigilance (PV). This review looks at how artificial intelligence (AI) technologies, such as machine learning (ML), natural language processing (NLP), and big data analytics, address current problems in pharmacovigilance (PV), such as underreporting, massive data volumes, and inefficient data processing. By automating data collection, real-time adverse event detection, and risk prediction, Al enhances drug safety through proactive risk management. Al's role in PV is developing quickly, offering more effective and precise drug safety monitoring despite obstacles in data quality, model interpretability, and regulatory compliance. The article's brief synopsis discusses how pharmacovigilance (PV) is being transformed by artificial intelligence (AI) through improved drug-related adverse event detection, analysis, and prediction. This review emphasizes how Al improves drug safety by increasing productivity, reducing human error, and facilitating real-time analysis of large datasets from various sources.

Keywords: PV(Pharmacovigilance), AI(artificial intelligence), signal detection, predictive analytics, natural language processing (NLP), Drug interaction, Adverse effect.