Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. 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 working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

Tang A ，Tam R ，Cadrin-Chênevert A ，Guest W ，Chong J ，Barfett J ，Chepelev L ，Cairns R ，Mitchell JR ，Cicero MD ，Poudrette MG ，Jaremko JL ，Reinhold C ，Gallix B ，Gray B ，Geis R ，Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group ... -  《-》

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 ... -  《-》

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 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 ... -  《-》

Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.

Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.

Thrall JH ，Li X ，Li Q ，Cruz C ，Do S ，Dreyer K ，Brink J ... -  《-》