Talk Title: Biomedical Data Science: The Hope, Hype, and Promise

Abstract: Data science was introduced as a “new” profession in the early 1960s, yet the Office of Data Science Strategy at the National Institutes of Health (NIH) was not established until 2018. Artificial intelligence (AI) traces its roots to at least the 1950s, with its first application as an expert system in medicine in the early 1970s. Yet, the NIH Working Group on AI was not established until 2018 as well, with their first report issued in 2019. This is not to say that these topics have been neglected over the past half-century, but it begs the question of why have they only now come to the forefront of biomedicine? We have seen multiple AI booms and busts, each of which has caused massive hype, dream crushing failures, and notable advancements (some more flashy than others) along the way. This talk is not intended to be a history lesson, but it will provide some context for how we got to where we are and what this says about where biomedical research and healthcare seems to be heading. I will further provide insight into how various innovations in computation, policy, and industry have pushed the field forward and where recently established programs to expand AI activities, such as AIM-AHEAD and Bridge2AI, may push the biomedical research community over the next decade. To ground this presentation, I will provide examples of how AI and data science more generally have driven activities at the All of Us Research Program and the Vanderbilt Health Data Science Center.

Short Bio: Bradley Malin is the Vice Chair for Research in the Department of Biomedical Informatics at Vanderbilt University Medical Center and is the Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science at Vanderbilt University. He founded and co-directs the Vanderbilt Health Data Science Center, as well as the Center for Genetic Privacy and Identity in Community Settings (GetPreCiSe), the latter of which is an NIH Center of Excellence in Ethical, Legal, and Social Implications Research. Since 2007, he has chaired the Committee on Access, Privacy, and Security (CAPS) for the NIH-sponsored All of Us Research Program and, in 2021, he became one of the founding PIs of the NIH Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD). He serves on various governmental advisory bodies, including the Board of Scientific Counselors of the National Center for Health Statistics (NCHS) of the U.S. Centers for Disease Control and Medicine (CDC). He was honored as a Presidential Early Career Award for Scientists and Engineers (PECASE) and is an elected member of the National Academy of Medicine (NAM), the American College of Medical Informatics (ACMI), the American Institute for Medical and Biological Engineering (AIMBE), and the International Academy of Health Sciences Informatics (IAHSI) . He received a B.S. in Biological Sciences, M.S. in Machine Learning, M.Phil. in Public Policy and Management, and Ph.D. in Computer Science, all from Carnegie Mellon University.