AI-Driven Big Data Analytics for Personalized Cancer Treatment: Integrating Multi-Omics, Medical Imaging, and Predictive Intelligence

Abstract

Cancer treatment still faces some major challenges - tumors are highly variable, diagnosis is slow, and treatments are not well personalized. Traditional cancer care is based on generalized treatment strategies and does not take into consideration the unique genetic, clinical, and environmental variations of each individual patient. Recent advances in big data analysis, artificial intelligence (AI), and machine learning (ML) have begun to bring personalized cancer care. In this shift, decisions are made using a large amount of medical data of varying nature. This paper provides an overview of big data frameworks based on Artificial Intelligence (AI) for the personalized treatment of cancer. It combines clinical data, multi-omics data, and advanced imaging. Using proven techniques and emerging research, the paper demonstrates how deep learning and predictive modeling, along with explainable AI, are involved in improving the early detection of cancer, predicting how patients will react to treatment, and planning individualized therapies. The results show that AI systems enhance diagnostic accuracy, improve risk identification, optimize treatments, and reduce side effects and wastage. However, there are still problems. Data differences, algorithm bias, the challenge of interpreting AI outputs, and rules for using AI in medicine are all issues that are still being debated. This research provides a single framework for AI-based precision oncology that can be scaled and provides practical advice for clinicians, policy makers, and future research.