Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

In the era of personalized and precision medicine, informatics technologies utilizing
machine learning (ML) and quantitative imaging are witnessing a rapidly increasing role in …

Machine learning (ML) has the potential to transform oncology and, more broadly, medicine.
1 The introduction of ML in health care has been enabled by the digitization of patient data …

The volume and complexity of scientific and clinical data in oncology have grown markedly
over recent years, including but not limited to the realms of electronic health data …

Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …

Imaging datasets in cancer research are growing exponentially in both quantity and
information density. These massive datasets may enable derivation of insights for cancer …

The increasing breadth and depth of resolution in biological and clinical data, including‐
omics and real‐world data, requires advanced analytical techniques like artificial …

The concept of precision oncology involves the prescription of therapies that target the
molecular driver alterations of an individual patient's tumor. This treatment paradigm has …

Machine learning-based tools are capable of guiding individualized clinical management
and decision-making by providing predictions of a patient's future health state. Through their …

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …