News

Keep up with the latest news from the NCI Center for Biomedical Informatics and Information Technology (CBIIT) and the data science communities.

Learn how NIH and NCI researchers developed TrialGPT, an artificial intelligence algorithm that could help clinicians connect patients to clinical trial opportunities more efficiently.

NCI-funded researchers developed a new model called “SEQUOIA” that’s helping capture detailed data from whole slide images. See how this technology could someday be the solution to better biopsies.

Explore a new artificial intelligence model for detecting gliomas in tissue samples during brain surgeries.

Listen to how NCI’s Small Business Innovation Research program is funding technology to enhance breast cancer detection and prevention.

The hallmark of a good AI model is its ability to work the same in different groups, settings, and situations. See how these NCI researchers used in-house and external images to test their prostate model’s generalizability.

Want to learn more about bioinformatics? Tap into these two newly published articles to see what some prominent researchers are saying about the field and where it’s headed.

Construction of the Advanced Research Projects Agency for Health’s (ARPA-H’s) BDF Toolbox program is well underway. ARPA-H has awarded contracts to nearly 20 teams—representing academia, nonprofits, and commercial organizations—who are tackling a broad range of projects, many of which are directly related to cancer research.

Thanks to funding from NCI’s Small Business Innovation Research program, a new tool recently received FDA clearance. See how this cyber device could help you with artificial intelligence (AI)-guided glioblastoma segmentation.

An international team of researchers combined genomics, biopsy results, and artificial intelligence (AI) to track prostate cancer over time. Learn more about these “evolvability” metrics and how they could someday help predict cancer re-occurrence.

NCI-funded researchers are blending mathematics with machine learning to refine cancer treatment. In the future, this kind of virtual tumor model could help to further personalize care for people with cancer.