Gayatri Mahale, Nikhil Chaudhari, Utkarsh Mandage, Jagruti Patil, Snehal Chaudhari, Harshal Borse, Dr. Pankaj Chaudhari, Sarita Beldar
Throughout the whole life cycle of a pharmaceutical product, pharmacovigilance is essential for guaranteeing drug safety and advancing patient wellbeing. However, this subject confronts a number of difficulties, such as underreporting of unfavourable events, poor data quality, and the difficulty of signal detection in massive datasets. There is rising interest in utilising the potential of generative artificial intelligence (AI) tools to address these issues and improve medication safety monitoring. This article investigates the uses and effects of generative AI in pharmacovigilance. In addition to demonstrating their capacity to evaluate drug databases, medical literature, and real-world data sources to detect medication interactions, adverse events, and potential safety signals, it gives an overview of common generative models and explains how they function. Additionally, it emphasises the value of expert oversight and human validation when interpreting and acting on the insights produced by generative AI systems. Combining generative AI's computational capability with human expertise results in a synergistic approach when it is used with conventional pharmacovigilance techniques. Improved signal identification, effective case report production, pro-active risk assessment, and optimal resource allocation are all possible outcomes of this integration. The essay also discusses difficulties with data quality, interpretability, and model validation in generative AI applications, highlighting the necessity of standardised procedures and coordinated efforts from all parties. Overall, generative AI has enormous potential for pharmacovigilance.
Article Details
Unique Paper ID: 161506

Publication Volume & Issue: Volume 10, Issue 4

Page(s): 423 - 430
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