Christopher P. Austin, MD - Director, National Center for Advancing Translational Sciences

A bedrock principle at NCATS is that translation is complete only when interventions reach and benefit all the individuals and communities that need them. This critical step of the translational process, referred to as Dissemination and Implementation (D&I), often is stalled by a lack of inclusion in clinical trials, hindering the development of effective new interventions and preventing existing ones from reaching the people for whom they were created.

Guest post by Susan Gregurick, PhD, Associate Director for Data Science and Director, Office of Data Strategy, NIH

NIH National Library of Medicine - Musings from the Mezzanine 

Some Insights on the Roles and Uses of Generalist Repositories


As the number of Americans infected with the novel coronavirus edges closer to 100,000 per day, we must draw attention to a fundamental deficiency in our collective response to the outbreak of SARS-CoV-2: Policymakers at all levels of government–from local to state to federal–lack even the most basic up-to-date information to make informed decisions regarding our collective health.

Michael G. Kurilla, MD, PhD; Director of the Division of Clinical Innovation, NCATS

With the National COVID Cohort Collaborative (N3C), the goal is to collect and harmonize electronic clinical, laboratory and diagnostic data from hospitals and health care institutions around the nation following the highest standards for data security and confidentiality.

Christopher P. Austin, MD - Director, National Center for Advancing Translational Sciences

Two favorite quotes are at the top of my mind this month: “Much is known, but unfortunately in different heads.” and “Be the change you want to see in the world.”

Benjamin Amor

Most often, when people talk about artificial intelligence (AI) they mean a particular kind of AI called machine learning (ML). This is the process of uncovering patterns in data using algorithms and then using those algorithms to make predictions about new data. For example, the recent advances in mammography for the diagnosis of breast cancers, taking a collection of images from CT scans and trying to find the features that discriminate the benign and malignant tumours.