The integration of Artificial Intelligence (AI) in the regulatory approval process has emerged
as a transformative paradigm, revolutionizing industries and institutions worldwide. This
abstract provides an overview of the multifaceted impact of AI on regulatory approval
procedures across diverse sectors. AI technologies, encompassing machine learning, natural
language processing, and data analytics, have streamlined and expedited the regulatory
landscape. They enable efficient analysis of vast data sets, facilitating more informed decision-
making by regulatory agencies. AI-driven predictive models assess risks, forecast outcomes,
and identify potential safety concerns in pharmaceuticals, medical devices, and food products.
These capabilities enhance the assessment of product efficacy and safety profiles, leading to
more accurate, evidence-based approvals. Furthermore, AI augments regulatory compliance
and monitoring efforts. It enables real-time surveillance of adverse events and anomalous
patterns, ensuring timely interventions and mitigating risks. Automated compliance checks,
powered by AI algorithms, enhance the scrutiny of submissions and adherence to regulatory
guidelines. However, challenges related to data privacy, algorithmic transparency, and bias
mitigation must be vigilantly addressed to maintain integrity and fairness in the decision-
making process. The infusion of AI into regulatory approval processes marks a pivotal
advancement with profound implications. By enhancing data-driven decision-making,
improving compliance monitoring, and redefining stakeholder interactions, AI accelerates the
pace and precision of regulatory approvals. While challenges persist, the judiciousincorporation of AI holds the potential to usher in a new era of efficacy, safety, and accessibility
across industries that are subject to regulatory oversight.
Keywords: Artificial Intelligence (AI), Regulatory Approval, Regulatory Affairs
Publication date: 01/09/2024
https://ijbpas.com/pdf/2024/September/MS_IJBPAS_2024_8305.pdf
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https://doi.org/10.31032/IJBPAS/2024/13.9.8305
