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 …

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …

Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Artificial intelligence and machine learning techniques are breaking into biomedical
research and health care, which importantly includes cancer research and oncology, where …

Underserved populations experience higher levels of pain. These disparities persist even
after controlling for the objective severity of diseases like osteoarthritis, as graded by human …

Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …

Background Artificial intelligence (AI) applications are growing at an unprecedented pace in
health care, including disease diagnosis, triage or screening, risk analysis, surgical …

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics.
Faster whole slide image scanning has paved the way for this development, but …