A Review on Artificial Intelligence in Drug Discovery and Personalized Medicine

Integration of Artificial Intelligence (AI) into pharmaceutical and healthcare sectors has catalyzed a paradigm alteration in how drugs are discovered, developed, and prescribed. AI offers a transformative alternative by enabling rapid data analysis, pattern recognition, and predictive modeling across vast biomedical datasets, thereby significantly accelerating the drug development pipeline. In parallel, the emergence of personalized medicine—an approach that tailors management to unique genetic, environmental, and lifestyle issues of distinct patients—has gained momentum as a means to enhance therapeutic efficacy and reduce adverse effects. To aid in clinical decision-making, AI is crucial in this field since it simplifies the integration and interpretation of complicated multi-omics data (e.g., genomes, transcriptomics, proteomics), EHRs, and RWE. Our goal in writing this review is to give readers a bird's-eye view of how artificial intelligence has revolutionized drug development and customized medicine. Additionally, real-world case studies are examined to illustrate successful implementations of AI in clinical and research settings. The review also addresses the major challenges associated with AI adoption, including data quality, model interpretability, regulatory concerns, and ethical implications. Finally, it outlines future directions and emerging trends that are poised to shape the next generation of AI-driven biomedical innovation.