Integrating Pharmacogenomics and AI: A Review on Personalized Drug Response and Clinical Implementation in Precision Medicine

This research examines the integration of pharmacogenomics and artificial intelligence (AI) in supporting the development of precision medicine, particularly to predict different drug responses in individuals. The study aims to synthesize the development of AI methods, clinical applications, and implementation challenges in pharmacogenomics. The method used is a Systematic Literature Review (SLR) based on the PRISMA framework of 34 Scopus-indexed scientific articles published during the period 2021–2025. The study results indicate that AI models, especially machine learning and deep learning, can predict drug responses with high accuracy and enhance patient stratification and integration into clinical decision support systems. The integration of multi-omics data also improves model performance and transparency through explainable AI approaches. These findings affirm the significant potential of AI-pharmacogenomics in enhancing more personalized, efficient, and evidence-based therapy, while also highlighting the importance of data governance, cross-population validation, and ethical and regulatory frameworks for responsible clinical implementation.