Precision Medicine in Prostate Cancer: Integrating Genomics, AI, and Big Data for Personalized Treatment

Abstract

Prostate cancer (PCa) remains one of the most prevalent cancers among men worldwide, exhibiting significant heterogeneity in its molecular profile and clinical course. Traditional approaches to treatment have often been generalized, leading to variable outcomes and, at times, unnecessary overtreatment. Precision medicine promises to transform PCa management by leveraging genomics, artificial intelligence (AI), and big data to tailor treatments to each patient’s molecular profile. This review examines how genomics has enhanced our understanding of PCa, identifying critical genetic mutations and molecular subtypes that influence disease progression. Additionally, the application of AI and machine learning in analyzing complex datasets has proven instrumental in discovering novel biomarkers, optimizing therapeutic choices, and predicting patient responses. The integration of big data from multiple platforms, including genomics, imaging, and electronic health records (EHRs), offers an unprecedented level of insight into the nuances of PCa. We discuss key genomic biomarkers, emerging AI-based predictive models, and the role of big data in advancing PCa precision medicine. Finally, we explore the challenges of clinical implementation, including data privacy, ethical concerns, and the need for interdisciplinary collaboration. The insights from this review underscore the transformative potential of precision medicine in enhancing prostate cancer treatment outcomes and the necessity for further research to overcome existing limitations. Keywords: Prostate cancer, precision medicine, genomics, artificial intelligence, big data, personalized treatment, biomarkers, molecular subtypes, machine learning.