Literature DB >> 29655580

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

An Tang1, Roger Tam2, Alexandre Cadrin-Chênevert3, Will Guest4, Jaron Chong5, Joseph Barfett6, Leonid Chepelev7, Robyn Cairns8, J Ross Mitchell9, Mark D Cicero6, Manuel Gaudreau Poudrette10, Jacob L Jaremko11, Caroline Reinhold5, Benoit Gallix5, Bruce Gray6, Raym Geis12.   

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.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Healthcare; Imaging; Machine learning; Medicine; Quality improvement; Radiology

Mesh:

Year:  2018        PMID: 29655580     DOI: 10.1016/j.carj.2018.02.002

Source DB:  PubMed          Journal:  Can Assoc Radiol J        ISSN: 0846-5371            Impact factor:   2.248


  65 in total

1.  Artificial intelligence and diagnosis in general practice.

Authors:  Nick Summerton; Martin Cansdale
Journal:  Br J Gen Pract       Date:  2019-07       Impact factor: 5.386

2.  Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Authors:  Walter F Wiggins; M Travis Caton; Kirti Magudia; Sha-Har A Glomski; Elizabeth George; Michael H Rosenthal; Glenn C Gaviola; Katherine P Andriole
Journal:  Radiol Artif Intell       Date:  2020-11-04

Review 3.  Demystification of AI-driven medical image interpretation: past, present and future.

Authors:  Peter Savadjiev; Jaron Chong; Anthony Dohan; Maria Vakalopoulou; Caroline Reinhold; Nikos Paragios; Benoit Gallix
Journal:  Eur Radiol       Date:  2018-08-13       Impact factor: 5.315

4.  Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

Authors:  Cumali Aktolun
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12       Impact factor: 9.236

5.  Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases.

Authors:  D Zopfs; K Laukamp; R Reimer; N Grosse Hokamp; C Kabbasch; J Borggrefe; L Pennig; A C Bunck; M Schlamann; S Lennartz
Journal:  AJNR Am J Neuroradiol       Date:  2022-01-06       Impact factor: 3.825

6.  Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions.

Authors:  Rohil Malpani; Christopher W Petty; Neha Bhatt; Lawrence H Staib; Julius Chapiro
Journal:  Dig Dis Interv       Date:  2021-07-17

Review 7.  Ethical considerations for artificial intelligence: an overview of the current radiology landscape.

Authors:  Tugba Akinci D'Antonoli
Journal:  Diagn Interv Radiol       Date:  2020-09       Impact factor: 2.630

Review 8.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2020-06-19

9.  Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.

Authors:  Joseph N Stember; Haydar Celik; David Gutman; Nathaniel Swinburne; Robert Young; Sarah Eskreis-Winkler; Andrei Holodny; Sachin Jambawalikar; Bradford J Wood; Peter D Chang; Elizabeth Krupinski; Ulas Bagci
Journal:  Radiol Artif Intell       Date:  2020-11-11

10.  A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence.

Authors:  Mutlu Demirer; Sema Candemir; Matthew T Bigelow; Sarah M Yu; Vikash Gupta; Luciano M Prevedello; Richard D White; Joseph S Yu; Rainer Grimmer; Michael Wels; Andreas Wimmer; Abdul H Halabi; Alvin Ihsani; Thomas P O'Donnell; Barbaros S Erdal
Journal:  Radiol Artif Intell       Date:  2019-11-27
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