INTEGRATING AI SYSTEMS IN PERSONALIZED HEALTHCARE: FROM DATA ANALYTICS TO TREATMENT OPTIMIZATION

Abstract

Including artificial intelligence (AI) into customized medicine marks a paradigm change in healthcare delivery and gives hitherto unheard-of chances to customize medical treatments depending on particular patient traits. To maximize therapy options, this thorough paper investigates the transforming possibilities of artificial intelligence technologies in assessing complicated patient data comprising genomic profiles, electronic health records, and clinical biomarkers. Particularly in oncology and chronic disease management, this paper highlights how remarkably capable machine learning algorithms and advanced neural networks have been in disease risk assessment, treatment response prediction, and therapeutic optimization across many medical conditions. The paper emphasizes effective applications in healthcare environments where artificial intelligence-driven methods have improved diagnosis accuracy and treatment results while lowering side effects. Along with possible answers come critical issues such as data privacy problems, algorithmic bias, and the need for strong validation systems. The results imply that the effective application of artificial intelligence in individualized medicine calls for a well-coordinated strategy combining ethical considerations, clinical knowledge, and technical innovation. This paper summarizes present data from reputable medical institutions and research centers, thereby giving researchers and practitioners practical insights to progress the discipline of customized medicine driven by artificial intelligence.