Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.

Jaremko JL ，Azar M ，Bromwich R ，Lum A ，Alicia Cheong LH ，Gibert M ，Laviolette F ，Gray B ，Reinhold C ，Cicero M ，Chong J ，Shaw J ，Rybicki FJ ，Hurrell C ，Lee E ，Tang A ，Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group ... -  《-》

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

Geis JR ，Brady AP ，Wu CC ，Spencer J ，Ranschaert E ，Jaremko JL ，Langer SG ，Kitts AB ，Birch J ，Shields WF ，van den Hoven van Genderen R ，Kotter E ，Gichoya JW ，Cook TS ，Morgan MB ，Tang A ，Safdar NM ，Kohli M ... -  《-》

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Geis JR ，Brady AP ，Wu CC ，Spencer J ，Ranschaert E ，Jaremko JL ，Langer SG ，Borondy Kitts A ，Birch J ，Shields WF ，van den Hoven van Genderen R ，Kotter E ，Wawira Gichoya J ，Cook TS ，Morgan MB ，Tang A ，Safdar NM ，Kohli M ... -  《-》

Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations.

Parker W ，Jaremko JL ，Cicero M ，Azar M ，El-Emam K ，Gray BG ，Hurrell C ，Lavoie-Cardinal F ，Desjardins B ，Lum A ，Sheremeta L ，Lee E ，Reinhold C ，Tang A ，Bromwich R ... -  《-》

Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles.

Parker W ，Jaremko JL ，Cicero M ，Azar M ，El-Emam K ，Gray BG ，Hurrell C ，Lavoie-Cardinal F ，Desjardins B ，Lum A ，Sheremeta L ，Lee E ，Reinhold C ，Tang A ，Bromwich R ... -  《-》