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@article{168731,
        author = {Miss Patil Jyoti Narayan and Mrs Asmi M Kadam and Miss Snehal Trimbak Daud and Ms Minakshi Ajamal Jadhav and Mr  Kuldeep Sukhdeo Kanadje},
        title = {PHARMACOVIGILANCE IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1839-1847},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168731},
        abstract = {Summarize the key points of the review, including the role of AI and ML in improving drug safety, challenges in traditional pharmacovigilance, current applications, and future directions. Pharmacovigilance (PV), the science of detecting, assessing, understanding, and preventing adverse drug reactions (ADRs), plays a critical role in ensuring patient safety and drug efficacy. Traditionally, pharmacovigilance has relied heavily on manual reporting systems, spontaneous reports, and clinical trial data, all of which have limitations such as underreporting, delays in signal detection, and the inability to manage large datasets efficiently. However, the advent of artificial intelligence (AI) and machine learning (ML) is revolutionizing the pharmacovigilance landscape by providing advanced tools for automating data collection, processing vast amounts of realworld data (RWD), and predicting ADRs with greater speed and accuracy. 
This review explores the transformative impact of AI and ML technologies on pharmacovigilance processes. AIpowered tools such as natural language processing (NLP), text mining, and predictive modeling allow for the automation of adverse event reporting, rapid signal detection, and integration of diverse data sources, including electronic health records (EHRs), social media, and patientreported outcomes. The use of machine learning algorithms, such as decision trees, random forests, and deep learning networks, has enabled Realtime monitoring and detection of safety signals, significantly improving the efficiency of post marketing surveillance.},
        keywords = {Pharmacovigilance, Artificial Intelligence,  (AI)   Machine Learning (ML), Adverse Drug Reactions (ADRs),  Signal Detection,  Data Mining, Natural Language Processing (NLP), Predictive Analytics, Real-Time Monitoring},
        month = {October},
        }