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@article{204621,
        author = {P Prameela},
        title = {The Evolving Role of Artificial Intelligence in Pharmaceutical Regulatory Decision-Making: Opportunities and Challenges in Drug Approval},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2978-2982},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204621},
        abstract = {Artificial Intelligence (AI) has emerged as a transformative technology in pharmaceutical regulatory affairs, significantly influencing drug development, regulatory submissions, clinical trial management, pharmacovigilance, and regulatory decision-making. The increasing complexity of regulatory requirements and the growing volume of scientific data have created challenges for traditional regulatory systems. AI technologies, including machine learning (ML), natural language processing (NLP), predictive analytics, and deep learning, provide innovative solutions to enhance efficiency, accuracy, and transparency throughout the regulatory lifecycle. Regulatory agencies such as the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), and Central Drugs Standard Control Organization (CDSCO) are increasingly exploring AI-driven approaches to optimize drug evaluation and approval processes. AI applications facilitate automated data analysis, regulatory intelligence, dossier preparation, safety signal detection, and real-world evidence generation, thereby accelerating regulatory review timelines. However, widespread implementation of AI also presents challenges related to data integrity, algorithm transparency, cyber security, validation, ethical concerns, and regulatory compliance. The absence of globally harmonized regulatory frameworks further complicates the adoption of AI technologies in pharmaceutical regulation. This review critically evaluates the opportunities and challenges associated with AI integration into regulatory affairs and highlights future regulatory considerations necessary for safe, ethical, and effective implementation. The study emphasizes the need for standardized validation procedures, explainable AI models, global regulatory harmonization, and collaborative stakeholder engagement to maximize the benefits of AI in drug approval processes.},
        keywords = {Artificial Intelligence, Regulatory Affairs, Drug Approval, Machine Learning, Pharmacovigilance, Clinical Trials, Regulatory Intelligence, FDA, EMA, Digital Regulatory Science.},
        month = {June},
        }