Literature DB >> 29599010

Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.

Shahein H Tajmir1, Tarik K Alkasab2.   

Abstract

Radiology practice will be altered by the coming of artificial intelligence, and the process of learning in radiology will be similarly affected. In the short term, radiologists will need to understand the first wave of artificially intelligent tools, how they can help them improve their practice, and be able to effectively supervise their use. Radiology training programs will need to develop curricula to help trainees acquire the knowledge to carry out this new supervisory duty of radiologists. In the longer term, artificially intelligent software assistants could have a transformative effect on the training of residents and fellows, and offer new opportunities to bring learning into the ongoing practice of attending radiologists.
Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Keywords:  Artificial intelligence; education; machine learning

Mesh:

Year:  2018        PMID: 29599010     DOI: 10.1016/j.acra.2018.03.007

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  13 in total

1.  Medical students' attitude towards artificial intelligence: a multicentre survey.

Authors:  D Pinto Dos Santos; D Giese; S Brodehl; S H Chon; W Staab; R Kleinert; D Maintz; B Baeßler
Journal:  Eur Radiol       Date:  2018-07-06       Impact factor: 5.315

2.  Natural Language Processing of Radiology Text Reports: Interactive Text Classification.

Authors:  Walter F Wiggins; Felipe Kitamura; Igor Santos; Luciano M Prevedello
Journal:  Radiol Artif Intell       Date:  2021-05-12

Review 3.  Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Authors:  Yasasvi Tadavarthi; Valeria Makeeva; William Wagstaff; Henry Zhan; Anna Podlasek; Neil Bhatia; Marta Heilbrun; Elizabeth Krupinski; Nabile Safdar; Imon Banerjee; Judy Gichoya; Hari Trivedi
Journal:  Radiol Artif Intell       Date:  2022-02-02

Review 4.  Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

Authors:  Ling Yang; Ioana Cezara Ene; Reza Arabi Belaghi; David Koff; Nina Stein; Pasqualina Lina Santaguida
Journal:  Eur Radiol       Date:  2021-09-21       Impact factor: 5.315

Review 5.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

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

6.  A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Authors:  Walter F Wiggins; M Travis Caton; Kirti Magudia; Michael H Rosenthal; Katherine P Andriole
Journal:  J Digit Imaging       Date:  2021-07-29       Impact factor: 4.903

7.  Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey.

Authors:  Ruben Pauwels; Yumi Chokyu Del Rey
Journal:  Dentomaxillofac Radiol       Date:  2021-01-12       Impact factor: 3.525

8.  Developing a curriculum in artificial intelligence for emergency radiology.

Authors:  Edmund M Weisberg; Elliot K Fishman
Journal:  Emerg Radiol       Date:  2020-08

9.  The Importance of the Clinical Internship for the Radiologist.

Authors:  Andrew D Schweitzer; David Sarkany
Journal:  Acad Radiol       Date:  2020-06-30       Impact factor: 3.173

10.  Attitudes towards medical artificial intelligence talent cultivation: an online survey study.

Authors:  Dongyuan Yun; Yifan Xiang; Zhenzhen Liu; Duoru Lin; Lanqin Zhao; Chong Guo; Peichen Xie; Haotian Lin; Yizhi Liu; Yuxian Zou; Xiaohang Wu
Journal:  Ann Transl Med       Date:  2020-06
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