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 …

Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer
care. With recent advances in the field of artificial intelligence (AI), there is now a …

Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to
integration into clinical practice. This review describes the current state of the field, with a …

Abstract The role of Artificial Intelligence and Machine Learning in cancer research offers
several advantages, primarily scaling up the information processing and increasing the …

Technological advances have made it possible to study a patient from multiple angles with
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …

The multitude of multi-omics data generated cost-effectively using advanced high-
throughput technologies has imposed challenging domain for research in Artificial …

The data-driven identification of disease states and treatment options is a crucial challenge
for precision oncology. Artificial intelligence (AI) offers unique opportunities for enhancing …

The field of oncology has witnessed an extraordinary surge in the application of big data and
artificial intelligence (AI). AI development has made multiscale and multimodal data fusion …

Recently, a wide spectrum of artificial intelligence (AI)–based applications in the broader
categories of digital pathology, biomarker development, and treatment have been explored …