Artificial Intelligence In Pharmaceutical And Health Sciences: Applications, Challenges, And Governance

Abstract

Artificial intelligence (ai) transforms pharmaceutical research, from early-stage drug discovery to clinical trial optimization. Deep learning models enable identification of novel molecular targets, predictive toxicity modeling, and precision patient stratification. Despite these advances, ai introduces ethical, regulatory, and methodological challenges including algorithmic bias, reproducibility issues, and model opacity. This review critically examines the applications of ai in pharmaceutical sciences, highlights key case studies, and proposes actionable governance frameworks to ensure safe, transparent, and equitable adoption. Mock data illustrations and tables are included to demonstrate ai-driven predictions and decision-making processes.

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