Artificial Intelligence in Clinical Drug Decision-Making: Transforming Personalized Therapeutics

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology in clinical medicine, particularly in drug decision-making and personalized therapeutics. Traditional clinical decision-making often relies on clinician experience, standardized treatment guidelines, and population-based evidence, which may not adequately address patient-specific variability. AI-driven systems leverage machine learning, deep learning, and big data analytics to analyze complex clinical datasets, enabling optimized drug selection, dosing, and monitoring. This review explores the role of AI in clinical drug decision-making, highlighting its applications in personalized medicine, adverse drug reaction prediction, clinical workflow optimization, and decision support systems. The paper also discusses ethical challenges, data privacy concerns, regulatory issues, and future prospects of AI-assisted pharmacotherapy in clinical settings.

DOI: 10.8612/36.4.2021.2