Artificial Intelligence in Cancer Genomics: Transforming Diagnosis, Treatment, and Precision Medicine

Abstract

Artificial intelligence is revolutionizing the field of cancer genomics by enabling the rapid interpretation of complex and high-dimensional molecular data. Traditional genomic analyses often face limitations when processing the vast amount of sequencing information generated from tumor samples. AI algorithms, including machine learning and deep learning models, excel in identifying hidden patterns and associations within this data, leading to earlier and more accurate cancer diagnosis. By integrating genomic, transcriptomic, and epigenetic information, AI facilitates the classification of tumor subtypes, prediction of disease progression, and identification of actionable genetic alterations. In the context of treatment, AI supports the selection of personalized therapies by analyzing tumor-specific molecular features and matching them with appropriate drugs. This capability is central to the advancement of precision oncology, where individualized treatment strategies are tailored to each patient’s unique genetic profile. AI also contributes to ongoing treatment evaluation by monitoring response and detecting emerging resistance through longitudinal data, including liquid biopsy and circulating tumor DNA analyses. Furthermore, AI enhances clinical decision-making by powering predictive models and intelligent support systems that help oncologists design personalized treatment plans. These innovations improve patient outcomes, optimize therapeutic effectiveness, and reduce the burden of unnecessary interventions. As AI continues to integrate with cancer genomics, it is ushering in a transformative era in oncology, enabling more precise diagnosis, targeted therapies, and patient-centered care based on comprehensive molecular insights.