Peer-Reviewed Publication
J Imaging Inform Med2025;38(1):1-15.February 1, 2025Editorial
Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification.
David Clunie1, Fred Prior2, Michael Rutherford2, Stephen Moore3, William Parker4, Haridimos Kondylakis5, Christian Ludwigs6, Juergen Klenk7, Bob Lou8, Lawrence Tony O'Sullivan9, Dan Marcus10, Jiri Dobes11, Abraham Gutman12, Keyvan Farahani13
1PixelMed Publishing, Bangor, PA, USA. dclunie@dclunie.com.
2University of Arkansas for Medical Sciences, Little Rock, AR, USA.
3Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
4University of British Columbia, Vancouver, Canada.
5Institute of Computer Science, Foundation of Research & Technology - Hellas (FORTH), Heraklion, Greece.
6Aigora GmbH, Munich, Germany.
7Deloitte Consulting, New York, NY, USA.
8Google, Mountain View, CA, USA.
9IBIS, Princeton, NJ, USA.
10Flywheel, Minneapolis, MN, USA.
11John Snow Labs, Lewes, DE, USA.
12AG Mednet, Boston, MA, USA.
13National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Abstract
De-identification of medical images intended for research is a core requirement for data-sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the US National Cancer Institute (NCI) convened a virtual workshop with the intent of summarizing the state of the art in de-…
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