# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 1 Introduction  
Cancer represents one of the leading causes of morbidity, mortality, and healthcare expenditure worldwide [1]. According to the World Health Organization, cancer accounted for nearly 10 million deaths globally in 2020. Estimates from the OECD suggest that healthcare costs for oncological care in high-income countries are expected to exceed USD 500 billion annually by 2030 [2]. In worldwide healthcare systems, the rising incidence of cancer driven by an aging population and increased life expectancy is placing unprecedented strain on sustainability and resource allocation [3]. To meet this challenge, precisiononocology is undergoing a profound transformation powered by the convergence of genomics and artificial intelligence (AI). High-throughput sequencing technologies, such as next-generation sequencing (NGS), have revolutionized our understanding of tumor biology by enabling the identification of both somatic and germline pathogenic variants [4]. Meanwhile, AI, particularly through its subsets, machine learning (ML), which learns from data without explicit programming, and deep learning (DL), as a more advanced form utilizing artificial neural networks, is emerging as a critical partner in extracting actionable insights from complex, high-dimensional genomic datasets [5]. This synergy is already reshaping cancer care. From guiding the development of targeted therapies and predicting therapeutic response to enhancing early detection through circulating tumor DNA (ctDNA) analysis, AI-powered genomics is opening new frontiers in diagnosis, prognosis, and individualized treatment. However, the integration of these tools into clinical practice also poses challenges related to data standardization, interpretability, and equitable access. In light of these opportunities and limitations, there is a growing need to critically examine how genomics and AI are being implemented across the cancer care continuum. The necessity of this review emerges not only from the rapid evolution of these fields but also the heterogeneity of current knowledge, which often overlooks the full scope and complexity of their combined application [6; 7]. While existing reviews frequently focus on either genomics or AI in oncology, few offer a comprehensive synthesis that systematically analyzes the applications, benefits, and, crucially, the limitations of their synergy across all phases of cancer management, from risk assessment to resistance monitoring [8; 9]. The primary contribution of this review is to provide an updated and integrated overview of the transformative impact of genomics and AI in oncology, highlighting not only their synergies, which lead to personalized therapeutic strategies and improved early detection, but also the significant challenges that must be addressed for their full and equitable clinical implementation. These challenges include data standardization, the interpretability of AI algorithms, overcoming biases in datasets, and ethical considerations related to patient privacy [10].  
To guide the reader through this complex landscape, the manuscript is structured around the main domains of application of genomics and AI in oncology. We first examine the contributions of genomic technologies to cancer risk assessment, diagnosis, treatment (including targeted therapies and immunotherapy), prognosis, and early detection. We then explore the role of epigenetics as both a biomarker and a therapeutic target. Finally, we focus on how AI is leveraged to interpret genomic data, personalize treatments, enable drug repurposing, and drive therapeutic innovation. A dedicated section discusses current and future challenges, including ethical, technical, and clinical considerations, offering a comprehensive view of the path forward.