Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology Jacob L. Jaremko, MD, PhD,

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Presentation transcript:

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology Jacob L. Jaremko, MD, PhD, FRCPC, Marleine Azar, MSc, Rebecca Bromwich, PhD, LLM, LLB, Andrea Lum, MD, FRCPC, Li Hsia Alicia Cheong, MDCM, Martin Gibert, PhD, François Laviolette, PhD, Bruce Gray, MD, FRCPC, Caroline Reinhold, MD, MSc, FRCPC, Mark Cicero, MD, BESc, FRCPC, Jaron Chong, MD, MHI, FRCPC, James Shaw, PhD, Frank J. Rybicki, MD, PhD, Casey Hurrell, PhD, Emil Lee, MD, FRCPC, An Tang, MD, MSc, FRCPC Canadian Association of Radiologists Journal Volume 70, Issue 2, Pages 107-118 (May 2019) DOI: 10.1016/j.carj.2019.03.001 Copyright © 2019 The Authors Terms and Conditions

Figure 1 (A) Assuming proper respect of confidentiality and minimal risk associated with data sharing in scenarios where explicit consent is impractical, the balance between ethical principles may shift in the era of machine learning in radiology. (B) It is anticipated that the previous emphasis on fundamental rights of individuals may shift toward civic responsibility for public good. This figure is available in colour online at http://carjonline.org/. Canadian Association of Radiologists Journal 2019 70, 107-118DOI: (10.1016/j.carj.2019.03.001) Copyright © 2019 The Authors Terms and Conditions

Figure 2 Example of recognizable personal data hidden within magnetic resonance imaging images. (A) Raw data from an axial fluid-attenuated inversion recovery image. (B) Coronal 3-dimensional reconstruction from this data using a skin threshold, showing the subject's face. This figure is available in colour online at http://carjonline.org/. Canadian Association of Radiologists Journal 2019 70, 107-118DOI: (10.1016/j.carj.2019.03.001) Copyright © 2019 The Authors Terms and Conditions

Figure 3 Shifting paradigm: secondary use of electronic medical records in the machine learning paradigm can provide new insights in health care and potential commercial value. This figure is available in colour online at http://carjonline.org/. Canadian Association of Radiologists Journal 2019 70, 107-118DOI: (10.1016/j.carj.2019.03.001) Copyright © 2019 The Authors Terms and Conditions