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Do you work on a project funded by an active NIH grant? Is this project rooted in artificial intelligence, machine learning, and/or ethics components? Apply to this Notice of Special Interest by March 31, 2022.

Over the past several years, scientists have made exciting advances in the realm of artificial intelligence (AI) and its integration into the cancer imaging field. New AI tools could make cancer imaging faster, more accurate, and more informative. But are they ready for real-world implementation?

NCI’s Center for Cancer Research invites applicants with image processing, machine learning, and deep learning experience to be considered for a federal image bioinformatics scientist position supporting the Artificial Intelligence Resource. The successful candidate will develop and implement automated imaging and data processing workflows to analyze large image data sets generated by confocal microscopes.

Drs. Emily Greenspan and Eric Stahlberg of NCI’s CBIIT and Frederick National Laboratory for Cancer Research, respectively, recently contributed to an article, “Digital twins for predictive oncology will be a paradigm shift for precision cancer care,” published in Nature Medicine. The commentary examines the vision that members of NCI’s Envisioning Computational Innovations for Cancer Challenges (ECCIC) community have for developing cancer patient digital twins. Such a platform could revolutionize how clinicians and policymakers approach cancer care and further advance precision medicine.

In a recent podcast, NCI leaders from CBIIT and the Small Business Innovation Research Development Center shared how technological developments have enhanced cancer research and have helped usher in new diagnostics, treatments, and patient care.

A few of NCI’s Division of Cancer Biology grantees recently released publications on topics such as machine learning and artificial intelligence. These research results hold clues to how we research and develop various cancer treatments.

A new publication using NCI funding and resources shows that a machine learning model, called Panoptes, allowed cancer researchers to reliably predict subtypes of endometrial cancer. Such “computational pathology” offers a useful framework for supporting human pathologists, trimming the labor needed to interpret histological findings to under 4 minutes per slide, and eliminating the time and cost of genetic sequencing.

Cancer researchers and data scientists have the opportunity to provide NIH and the FDA input on the requirements for accelerating clinical applications of next generation sequencing and radiomics (including those using artificial intelligence and machine learning). Responses to these Requests for Information are due Monday, November 1, 2021.

NINR is promoting and improving the health of individuals, their families, and communities. Watch the videocast for any one of the three webinars to learn how AI can positively impact patient well-being and how clinicians can use this technology in their practice.

This RFI will inform the development of an NIH initiative on the use of Artificial Intelligence/Machine Learning to address health disparities and inequities and enhance diversity within the AI/ML workforce.