Abstract

FROM DETECTION TO PREDICTION: THE EVOLVING ROLE OF AI IN PHARMACOVIGILANCE SYSTEMS

Dr. Sahana V. M. Vats*, Dr. Sunil Kumar and Dr. Tribhuvan Pareek

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Abstract

Background: Pharmacovigilance (PV) serves as a cornerstone of public health, ensuring the safe use of medicinal products by detecting, assessing, and preventing adverse drug reactions (ADRs). However, traditional PV methods largely reliant on spontaneous reporting and manual signal detection are limited by underreporting, latency, and an inability to process unstructured, high-volume datasets in real time. Objective: This paper examines how Artificial Intelligence (AI) technologies are transforming pharmacovigilance from a reactive process to a predictive science. It explores AI’s potential to enhance ADR detection, automate signal processing, and support individualized drug safety monitoring. Methods: A narrative review was conducted using peer-reviewed literature, international pharmacovigilance reports, and official publications from organizations such as WHO, US FDA, and India’s Pharmacovigilance Programme of India (PvPI). Specific focus was given to machine learning, natural language processing (NLP), and real-world AI applications in pharmacovigilance systems, both globally and within the Indian healthcare framework. Results: AI enables automated data extraction from electronic health records, social media, and pharmacovigilance databases, significantly reducing time to signal detection. Machine learning models have demonstrated high sensitivity in identifying rare or unexpected ADRs, while NLP tools improve accuracy in classifying patient narratives and adverse event reports. In India, PvPI’s growing digital infrastructure presents a timely opportunity to integrate AI for proactive safety surveillance, including its potential applicability in the monitoring of AYUSH (Ayurveda, Yoga, Unani, Siddha, Homeopathy) medications. Conclusion: AI holds transformative promise for pharmacovigilance, offering a shift from passive detection to active prediction and prevention of ADRs. Strategic integration of AI into national PV frameworks, particularly in low- and middle-income countries, may redefine global drug safety standards in the coming decade.

Keywords: Pharmacovigilance, Artificial Intelligence, PvPI, Adverse Drug Reactions, Machine Learning, Drug Safety, Natural Language Processing.