Pharmacovigilance and Risk Management: A Study on AI-Based Drug Safety Monitoring

Er. Priyanshi

Indian Institute of Information Technology Guwahati (IIITG)s

Abstract

Pharmacovigilance has evolved significantly in recent decades, yet challenges remain in monitoring drug safety and managing risk in a timely manner. With the rapid advancement of artificial intelligence (AI) techniques, novel approaches to drug safety monitoring are emerging that promise to improve signal detection, risk evaluation, and post‐market surveillance. This study investigates the application of AI-based methods in pharmacovigilance, with a specific focus on risk management strategies to enhance patient safety. We review the state-of-the-art literature up to 2017, identify key methodological developments, and propose a simulation-based framework that integrates machine learning algorithms for signal detection with risk assessment models. Through statistical analysis, including a comparative table of performance metrics, our simulation research demonstrates that AI-based systems can offer increased sensitivity and specificity over traditional methods. The results indicate that such systems can provide early warnings of adverse drug reactions (ADRs), enabling regulatory bodies and healthcare providers to implement timely interventions. Our findings suggest that the integration of AI in pharmacovigilance is not only feasible but also highly promising in the context of risk management, although challenges related to data quality, algorithm transparency, and ethical considerations remain. The paper concludes with recommendations for future research and strategies for integrating AI into existing pharmacovigilance infrastructures.

Keywords

Pharmacovigilance; Artificial Intelligence; Drug Safety; Risk Management; Simulation Research

References

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