Machine Learning in Oncology: Methods, Applications, and Challenges

Clinical decisions have traditionally been guided by medical guidelines and accumulated experience. ML methods add rigor to this process; algorithms can generate individualized predictions by synthesizing data across broad patient bases. On a policy level, these insights can be used to inform data-driven guidelines and risk cohorts. On a more granular level, these insights enable a personalized approach to medicine that accounts for a patient’s unique characteristics.