Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles
William Parker, Jacob L. Jaremko, Mark Cicero, Marleine Azar, Khaled El-Emam, Bruce Gray, Casey Hurrell, Flavie Lavoie‐Cardinal, Benoit Desjardins, Andrea Lum, Lori Sheremeta, Emil Lee, Caroline Reinhold, An Tang, Rebecca Jaremko Bromwich
Abstract
The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively.