A.I-Driven Enhancements in Pharmacovigilance: Opportunities, Challenges, and Future Directions

Pharmacovigilance plays a critical role in ensuring drug safety by monitoring, detecting, and assessing Adverse Drug Reactions (ADRs). However, traditional pharmacovigilance systems often face challenges, including underreporting, delayed signal detection, and data overload. With the rapid advancement of Artificial Intelligence (AI), there is a growing interest in its potential to transform pharmacovigilance processes. This study explores the role of AI in enhancing pharmacovigilance, focusing on its current applications, key opportunities, challenges, and future directions. A comprehensive review of recent literature and case studies reveals that AI technologies, particularly machine learning, natural language processing, and deep learning, can significantly improve signal detection, automate data processing, and enable real-time surveillance of drug safety. Despite these promising developments, several challenges remain, including data quality issues, algorithm transparency, ethical concerns, and regulatory uncertainty. The study concludes that while AI offers transformative potential for pharmacovigilance, its implementation must be guided by robust validation frameworks, interdisciplinary collaboration, and updated regulatory policies to ensure the safe and effective integration of AI into existing drug safety systems. Recommendations for future research and strategic pathways for the responsible adoption of AI in pharmacovigilance are also discussed.