Empowering Pharmacovigilance: Unleashing the Potential of Generative AI in Drug Safety Monitoring

John Praveen

doi:10.37022/jiaps.v8i2.464

John Praveen Accenture Life sciences R&D Solutions, Department of Pharmacovigilance, Bangalore, India

DOI https://doi.org/10.37022/jiaps.v8i2.464

Abstract

Pharmacovigilance plays a crucial role in ensuring drug safety and promoting patient well-being throughout the life cycle of medicinal products. However, this field faces several challenges, including underreporting of adverse events, data quality issues, and the complexity of signal detection in large datasets. To address these challenges and enhance drug safety monitoring, there is a growing interest in harnessing the potential of generative artificial intelligence (AI) techniques. This article explores the applications and implications of generative AI in pharmacovigilance. It provides an overview of popular generative models and their working principles, highlighting their ability to analyse drug databases, medical literature, and real-world data sources to identify drug-drug interactions, adverse events, and potential safety signals. Moreover, it emphasizes the importance of human validation and expert oversight in interpreting and acting on the insights generated by generative AI algorithms. The integration of generative AI with traditional pharmacovigilance methods creates a synergistic approach, combining the computational power of AI with human expertise. This integration can lead to improved signal detection, efficient case report generation, proactive risk assessment, and optimized resource allocation. Additionally, the article addresses challenges related to data quality, interpretability, and model validation in generative AI applications, emphasizing the need for standardized protocols and collaborative efforts among stakeholders. Overall, the potential of generative AI in pharmacovigilance is vast. By leveraging its capabilities, we can enhance drug safety monitoring, facilitate early detection of adverse events, and improve patient outcomes. However, it is crucial to address ethical considerations, ensure data privacy, and maintain human oversight to foster responsible and effective implementation of generative AI in pharmacovigilance practices.

Keywords: Pharmacovigilance, Machine Learning in Pharmacovigilance, Signal Detection,, Risk Assessment, Drug Surveillance, Risk Management, Risk Managemen, Pharmacovigilance Automation, safety signal, Safety Signal Identification, Post-Marketing Surveillance, Pharmacovigilance Algorithms, Signal Prioritization

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Author Biography

John Praveen, Accenture Life sciences R&D Solutions, Department of Pharmacovigilance, Bangalore, India

Accenture Life Sciences R&D Solutions, Department of Pharmacovigilance, Bangalore, India

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