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Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence
Jefferson R Wilson1, Luciano M Prevedello1, Christopher D Witiw1
1From the Division of Neurosurgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), St Michael's Hospital, 30 Bond St, Toronto, ON, Canada M5B 1W8; Department of Surgery (J.R.W., C.D.W.) and Department of Medical Imaging (E.C.), University of Toronto, Toronto, Canada (J.R.W., C.D.W.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.).
Abstract:
In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided. Keywords: Artificial Intelligence, Data Liberation, Crowdsourcing © RSNA, 2023.
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