Artificial Intelligence in Genomic Medicine: From Data Integration to Clinical Decision Support in Oncology

Abstract

The integration of artificial intelligence with genomic medicine has opened new frontiers in the diagnosis, prognosis, and treatment of cancer. This review examines the current state and future potential of AI-driven genomic analysis in oncology, focusing on three key areas: the role of machine learning in identifying genetic patterns associated with tumor progression, the application of deep learning in predicting treatment response and resistance mechanisms, and the development of integrated clinical decision support systems. We discuss technical challenges, including data heterogeneity, model interpretability, and regulatory considerations. We also explore ethical implications and propose a framework for responsible AI implementation in genomic oncology. The review concludes that AI, when properly integrated with genomic data, can significantly advance personalized cancer care, though interdisciplinary collaboration, robust validation, and transparent governance are essential for clinical translation.