Abstract
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.
| Original language | English (US) |
|---|---|
| Journal | Canadian Association of Radiologists Journal |
| DOIs | |
| State | Accepted/In press - Jan 1 2018 |
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Keywords
- Artificial intelligence
- Deep learning
- Healthcare
- Imaging
- Machine learning
- Medicine
- Quality improvement
- Radiology
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
Cite this
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. / Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group.
In: Canadian Association of Radiologists Journal, 01.01.2018.Research output: Contribution to journal › Article
}
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, Joseph 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
PY - 2018/1/1
Y1 - 2018/1/1
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
UR - http://www.scopus.com/inward/record.url?scp=85045189457&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045189457&partnerID=8YFLogxK
U2 - 10.1016/j.carj.2018.02.002
DO - 10.1016/j.carj.2018.02.002
M3 - Article
C2 - 29655580
AN - SCOPUS:85045189457
JO - Canadian Association of Radiologists Journal
JF - Canadian Association of Radiologists Journal
SN - 0846-5371
ER -