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.