AI in Precision Oncology: Enhancing Cancer Treatment Through Predictive Modeling and Data Integration

Venkata Sai Teja Yarlagadda

Abstract


Precision oncology aims to tailor cancer treatments based on the genetic makeup of the tumor and the individual patient. This paper investigates how AI and machine learning can enhance precision oncology by analyzing multi-omics data, including genomics, proteomics, and clinical data, to predict the most effective treatment options for cancer patients. We explore the use of predictive modeling techniques, such as random forests and deep neural networks, to forecast treatment responses and identify potential drug-resistant mutations. The paper also highlights the challenges in integrating diverse datasets and the ethical considerations of AI in clinical decision-making.


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