Machine Learning in Oncology: Methods, Applications, and Challenges

The objective of this review is to provide an overview of machine learning (ML) in oncology from a methods and applications perspective and to offer a framework for leveraging ML in clinical decision making. Knowledge Generated This review presents an overview of common ML algorithms and clinical data sources and discusses their relative merits. The data curation process is outlined, along with the technical challenges involved in working with large-scale health care data. Many aspects of oncology have benefited from these approaches, with applications ranging from early detection to treatment evaluation. Relevance ML presents an opportunity to transform cancer care through data-driven insights. This review provides practitioners with a practical view of the ML pipeline and its challenges.