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@article{174021,
        author = {Mrs. Ayesha Sayyed and Mr. Sofiyan Sayyad and Mr.Ayaz Sayyed},
        title = {Pharmacovigilance in the Era of Artificial Intelligence and Big Data: Innovations, Challenges, and Future Perspectives},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3039-3045},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174021},
        abstract = {Background: Pharmacovigilance plays a crucial role in ensuring drug safety by detecting, assessing, and preventing adverse drug reactions and medication errors. Traditional pharmacovigilance methods, such as spontaneous reporting and post-marketing surveillance, face limitations like underreporting, data fragmentation, and delayed risk detection.
Aim: This review explores how artificial intelligence, big data analytics, and machine learning are transforming pharmacovigilance, improving adverse drug reaction detection, predictive analytics, and drug safety monitoring.
Method: In this paper, we have studied recent research on Pharmacovigilance. We thoroughly used search engines like PubMed, Elsevier, Web Science, Google Scholar, Science Direct, Medline Plus, Google Open Access, Europe PMC, Hub Med, Scopus, Semantic Scholar, Shodhaganga, Science Open, and ScienceDirect. Keywords search during Pharmacovigilance, Artificial Intelligence, Data Analytics, Adverse Drug Reactions.
Results: AI and big data enable rapid ADR identification using electronic health records, social media, and patient registries. Regulatory agencies are integrating digital tools to enhance drug safety monitoring.
Conclusion: Future PV advancements will include genomics, personalized medicine, blockchain, and patient-centered approaches, ensuring safer drug use globally. Strengthening collaborations and refining ADR reporting mechanisms will improve pharmacovigilance effectiveness.},
        keywords = {Pharmacovigilance, artificial intelligence, data analytics, adverse drug reactions, machine learning, regulatory challenges.},
        month = {March},
        }