TY - JOUR
T1 - Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
AU - Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group
AU - Tang, An
AU - Tam, Roger
AU - Cadrin-Chênevert, Alexandre
AU - Guest, Will
AU - Chong, Jaron
AU - Barfett, Joseph
AU - Chepelev, Leonid
AU - Cairns, Robyn
AU - Mitchell, J. Ross
AU - Cicero, Mark D.
AU - Poudrette, Manuel Gaudreau
AU - Jaremko, Jacob L.
AU - Reinhold, Caroline
AU - Gallix, Benoit
AU - Gray, Bruce
AU - Geis, Raym
AU - O'Connell, Timothy
AU - Babyn, Paul
AU - Koff, David
AU - Ferguson, Darren
AU - Derkatch, Sheldon
AU - Bilbily, Alexander
AU - Shabana, Wael
N1 - Funding Information:
The work was supported by a Clinical Research Scholarship Salary Award from the Fonds de recherche du Québec en Santé and Fondation de l'association des Radiologistes du Québec (FRQR-ARQ #34939) to An Tang.
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Deep learning
KW - Healthcare
KW - Imaging
KW - Machine learning
KW - Medicine
KW - Quality improvement
KW - Radiology
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U2 - 10.1016/j.carj.2018.02.002
DO - 10.1016/j.carj.2018.02.002
M3 - Review article
C2 - 29655580
AN - SCOPUS:85045189457
VL - 69
SP - 120
EP - 135
JO - Canadian Association of Radiologists Journal
JF - Canadian Association of Radiologists Journal
SN - 0846-5371
IS - 2
ER -