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The framework is built on four pillars: first, an AI-augmented surveillance system that combines clinical data with social determinants of health to dynamically identify high-risk areas; second, adaptive licensing with evolving labels that use continuous real-world data submissions to update risk\u2013benefit profiles in near-real time; third, pharmacist-led surveillance networks employing secure, automated reporting systems to enhance early detection of misuse; and fourth, the incorporation of harm reduction metrics through partnerships with community organizations and non-traditional data sources. This dynamic, process-oriented approach enables timely regulatory adjustments, ensures better alignment with FDA's REMS and post-marketing requirements (PMRs), and addresses ethical concerns related to AI bias and patient privacy. 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