Introduction to Machine Learning in Cancer Care

Abstract

Machine learning (ML) is revolutionizing oncology by enhancing diagnostic accuracy, prognostic predictions, and personalized treatment strategies. This chapter explores the integration of ML into cancer care, focusing on its applications in medical imaging, molecular profiling, and treatment optimization. Advanced algorithms, such as convolutional neural networks (CNNs), have demonstrated diagnostic accuracy comparable to or surpassing human experts, while techniques like radiogenomics bridge imaging and genomic data for non-invasive diagnostics. Despite these advancements, challenges such as data heterogeneity, model interpretability, and ethical concerns—including patient privacy and algorithmic bias—remain significant barriers to clinical implementation.

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