Evidence-to-Decision in Pharmaceutical Regulatory Science: A Comparative Review of Artificial Intelligence, Clinical Evidence, Real-World Evidence, Pharmacovigilance, and Regulatory Decision-Making in Saudi Arabia, the United States, and the European Union

Author

Shraddha Vijay Jagtap , Darshan Pramod Werulkar, Shubham N. Kanawade


Abstract

 Modern pharmaceutical regulatory frameworks are undergoing a paradigm shift driven by the integration of Artificial Intelligence (AI) and heterogenous data streams. This review article provides a comprehensive comparative analysis of the regulatory systems of the Saudi Food and Drug Authority (SFDA), the United States Food and Drug Administration (US FDA), and the European Medicines Agency (EMA). We explore the evolution of evidence-to-decision frameworks, moving from traditional clinical trial data to the active integration of Real-World Data (RWD) and Real-World Evidence (RWE). Special emphasis is placed on how AI-driven advanced analytics optimize pharmacovigilance, accelerate safety signal detection, and refine benefit-risk assessments. By evaluating the structural and legislative differences among the SFDA, FDA, and EMA, this paper outlines a roadmap for the future of AI-driven, evidence-based pharmaceutical regulations.


Keywords

Artificial Intelligence; Regulatory Science; Real-World Evidence; Pharmacovigilance; SFDA; US FDA; EMA; Evidence-to-Decision.



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References


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