AI-Enabled Personalized Medicine: Integrating Genomics, Clinical Decision Support & Precision Therapeutics

Abstract

Personalized medicine aims to provide healthcare based on the biological and clinical
characteristics of individual patients. Advancement in genomics has made it possible to identify
genetic modifications related to disease risk, drug response, and treatment outcomes, but interpreting
large and complex datasets remains challenging. Artificial intelligence (AI) is useful in analyzing
genomic, clinical, and treatment-related information and connecting these data with clinical
decisions. This review examines the role of AI in personalized medicine, with a focus on genomic
analysis, clinical decision support, and precision therapeutics. AI applications in genomic medicine
include variant interpretation, disease-risk prediction, pharmacogenomics, and multi-omics analysis.
AI-based clinical decision support system can assist in risk assessment, diagnosis, treatment
selection, and patient monitoring. In precision therapeutics, AI facilitates support in drug selection,
dose optimization, treatment-response prediction, and treatment monitoring. The review also
discusses applications in cancer, cardiovascular diseases, diabetes, neurological disorders, and rare
diseases. Important challenges include data quality, privacy, algorithmic bias, explainability, clinical
validation, cost, and unequal access to advanced technologies. Future progress will depend on
reliable data integration, responsible AI development, clinical oversight, and appropriate regulation.
The integration of genomics, AI-based decision support, and precision therapeutics could contribute
to more personalized, responsive, and patient-centered healthcare.