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Cited by 522

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Tang, An, Tam, Roger, Cadrin-Chênevert, Alexandre, Guest, Will, Chong, Jaron, Barfett, Joseph, Chepelev, Leonid, Cairns, Robyn, Mitchell, J. Ross, Cicero, Mark D., Poudrette, Manuel Gaudreau, Jaremko, Jacob L., Reinhold, Caroline, Gallix, Benoit, Gray, Bruce, Geis, Raym, for the Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group, O'Connell, Timothy, Babyn, Paul, Koff, David, Ferguson, Darren, Derkatch, Sheldon, Bilbily, Alexander, Shabana, Wael·2018·Canadian Association of Radiologists Journal
DOI 10.1016/j.carj.2018.02.002
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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.

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Show all 34 references

Research works printed but not linked: 12

  1. Arthur S., 1959, IBM J Res Dev, 3, 535
  2. McCarthy J., 2006, AI Magazine, 27, 12
  3. Pickup LC, Gleeson F, Talwar A, Kadir T. Lung nodule risk stratification using CNNs: can we generalize from screening training data? Conference on Machine Intelligence in Medical Imaging. September 26–27, 2017; Baltimore, MD.
  4. Su J., 2017, arXiv
  5. Evtimov I., 2017, arXiv
  6. Nelson GS. Practical implications of sharing data: a primer on data privacy, anonymization, and de-identification. Paper 1884–2015. SAS Global Forum Proceedings 2015. Available at: https://support.sas.com/resources/papers/proceedings15/1884-2015.pdf. Accessed January 28, 2018.
  7. Merkel D., 2014, Linux J, 2014, 2
  8. Wang X., 2017, arXiv
  9. Prior F.W., 2013, Conf Proc IEEE Eng Med Biol Soc, 2013, 1282
  10. Shin H.-C., 2016, J Mach Learn Res, 17, 1
  11. Litjens G., 2017, arXiv
  12. Wang D., 2016, arXiv

Cited by

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Metadata

Authors
Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., Chepelev, L., Cairns, R., Mitchell, J. R., Cicero, M. D., Poudrette, M. G., Jaremko, J. L., Reinhold, C., Gallix, B., Gray, B., Geis, R., for the Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group, O'Connell, T., Babyn, P., Koff, D., Ferguson, D., Derkatch, S., Bilbily, A., & Shabana, W.
Venue
Canadian Association of Radiologists Journal
Publisher
SAGE Publications
Published
2018-05
Volume
69
Issue
2
Pages
120-135
ISSN
  • 0846-5371 (ISSN-L)
  • 1488-2361
DOI
10.1016/j.carj.2018.02.002
OpenAlex
W2796925382
Cited by
522
References
34
FWCI
9.07
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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CoChat. (2026). Citation check: Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. https://app.cochat.ai/papers/10.1016/j.carj.2018.02.002
The paper
Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., Chepelev, L., Cairns, R., Mitchell, J. R., Cicero, M. D., Poudrette, M. G., Jaremko, J. L., Reinhold, C., Gallix, B., Gray, B., Geis, R., for the Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group, O'Connell, T., Babyn, P., . . . Shabana, W. (2018). Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Canadian Association of Radiologists Journal. https://doi.org/10.1016/j.carj.2018.02.002

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.