| AI-Powered Genomics in Precision Oncology: Current Advances and Future Directions |
| Paper ID : 1237-IGA |
| Authors |
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Parinaz Khanjanpoor *1, Hesam Aminian2 1DEPARTMENT OF HEALTH SCIENCES (RELATED TO THE SCHOOL OF MEDICINE), UNIVERSITY OF PIEDMONT ORIENTALE (UPO), NOVARA ITALY 2Department of Health Sciences,School of Medicine |
| Abstract |
| Background and Aim: Artificial intelligence (AI) has emerged as a transformative tool in precision oncology, particularly in genomics, by enabling rapid and accurate analysis of complex cancer data. This review aims to summarize current advances in AI-powered genomics and explore future directions for its application in personalized cancer diagnosis and treatment. Materials and Methods: A systematic literature search was conducted using keywords such as "AI," "genomics," "precision oncology," and "cancer" across databases including PubMed and Google Scholar. Inclusion criteria were peer-reviewed articles published in English from 2015 to 2025, excluding conference proceedings. Results: AI algorithms integrated with next-generation sequencing facilitate identification of genetic mutations, molecular subtypes, and biomarkers critical for targeted therapies. These tools improve diagnostic accuracy and speed, enabling timely treatment decisions. AI-driven models predict treatment response and patient outcomes by analyzing multi-omics and clinical data, addressing tumor heterogeneity and resistance mechanisms. Additionally, AI aids in discovering novel biomarkers and therapeutic targets, accelerating drug development. Integration with digital pathology enhances biomarker quantification and immunotherapy selection. Despite challenges in data standardization and interpretability, AI applications are expanding into real-time monitoring and early cancer detection, promising more comprehensive personalized care. Conclusion: AI-powered genomics is revolutionizing precision oncology by enhancing data analysis, biomarker discovery, and treatment personalization. Continued advancements and interdisciplinary collaboration will overcome current challenges, facilitating clinical integration. This synergy holds great promise to improve cancer diagnosis, optimize therapies, and ultimately enhance patient outcomes. |
| Keywords |
| Keywords: Artificial intelligence, Genomics, Precision oncology, Cancer biomarkers, Personalized medicine |
| Status: Accepted |
