Abstract
The application of Artificial Intelligence (AI) in healthcare represents a significant advancement, enhancing our ability to transition from generalized treatment methods to more precise and personalized patient care. Harnessing vast quantities of data from genomics, clinical records, and patient records, AI and (ML) tools provide accurate diagnosis, optimize therapeutic strategy, and improve patient stratifying. Personalized medicine, propelled by advances in gene sequencing, biobanks, pharmacogenomics, and electronic medical records, customizes disease management to each patient's unique genetic and phenotypic profile and shifts health care from reactive to proactive care. Successful applications of AI in healthcare include predictive modelling for disease progression, personalized drug dosing, and drug repurposing. These AI-powered technologies, including predictive analytics, digital twins, robotic surgery, and virtual consultations, help streamline clinical workflows which can ultimately reduce medical errors enhance patient outcomes, and improve cost-effectiveness. Despite the many challenges in data quality, regulatory issues, and clinician-AI collaboration, the transformative potential of AI in personalized medicine underscores AI's ever-growing mission of providing precise, effective, and timely interventions suited to individual patients, hence becoming a principal driver of health care in the future.