News

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

NIH Institutes and Centers (including NCI) are interested in the validity, reliability, and utility of digital health tools and artificial intelligence/machine learning (AI/ML) technologies. You can apply for NCI funding to support the validation of these technologies for cancer research.

Are you planning to attend this year’s American Association for Cancer Research (AACR) Annual Meeting? Make sure to add these NCI-affiliated data science activities to your schedule!

Are you skilled in artificial intelligence and federated learning approaches? Come work here at NCI’s CBIIT as a bioinformatics specialist! Don’t miss out on this opportunity. Apply today!

Looking to become more familiar with artificial intelligence and GitHub for your data science needs? Check out these two courses from the Informatics Technology for Cancer Research Training Network.

What do you think of the updated Strategic Plan for Data Science from NIH? Respond to this Request for Information by March 15!

Participate in the upcoming challenge to assess NCI Cancer Research Data Commons' compatibility with AI/ML technologies.

Do you specialize in pancreatic cancer? See how researchers are using artificial intelligence to find undetectable cancers on scans of seemingly normal pancreases, long before clinical symptoms are visible.

A new algorithm is showing promising results in predicting lung cancer. See how proteins in your blood may someday help determine your risk for lung cancer.

Are you working on artificial intelligence (AI) solutions for cancer care? Check out these guidelines from the Office of the National Coordinator for Health Information, which should help make AI in clinical care more transparent.

CBIIT Director Dr. Tony Kerlavage and NCI Chief Information Officer Mr. Jeff Shilling discuss the need for standardized data; the importance of addressing biases in artificial intelligence (AI) models; recruitment; and the necessity for continuous learning when it comes to AI.