Literature DB >> 31072803

Impact of the rise of artificial intelligence in radiology: What do radiologists think?

Q Waymel1, S Badr1, X Demondion2, A Cotten2, T Jacques3.   

Abstract

PURPOSE: The purpose of this study was to assess the perception, knowledge, wishes and expectations of a sample of French radiologists towards the rise of artificial intelligence (AI) in radiology. MATERIAL AND
METHOD: A general data protection regulation-compliant electronic survey was sent by e-mail to the 617 radiologists registered in the French departments of Nord and Pas-de-Calais (93 radiology residents and 524 senior radiologists), from both public and private institutions. The survey included 42 questions focusing on AI in radiology, and data were collected between January 16th and January 31st, 2019. The answers were analyzed together by a senior radiologist and a radiology resident.
RESULTS: A total of 70 radiology residents and 200 senior radiologists participated to the survey, which corresponded to a response rate of 43.8% (270/617). One hundred ninety-eight radiologists (198/270; 73.3%) estimated they had received insufficient previous information on AI. Two hundred and fifty-five respondents (255/270; 94.4%) would consider attending a generic continuous medical education in this field and 187 (187/270; 69.3%) a technically advanced training on AI. Two hundred and fourteen respondents (214/270; 79.3%) thought that AI will have a positive impact on their future practice. The highest expectations were the lowering of imaging-related medical errors (219/270; 81%), followed by the lowering of the interpretation time of each examination (201/270; 74.4%) and the increase in the time spent with patients (141/270; 52.2%).
CONCLUSION: While respondents had the feeling of receiving insufficient previous information on AI, they are willing to improve their knowledge and technical skills on this field. They share an optimistic view and think that AI will have a positive impact on their future practice. A lower risk of imaging-related medical errors and an increase in the time spent with patients are among their main expectations.
Copyright © 2019 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  Artificial intelligence (AI); Machine learning; Radiologists; Survey

Mesh:

Year:  2019        PMID: 31072803     DOI: 10.1016/j.diii.2019.03.015

Source DB:  PubMed          Journal:  Diagn Interv Imaging        ISSN: 2211-5684            Impact factor:   4.026


  24 in total

1.  Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.

Authors:  Francesca Coppola; Lorenzo Faggioni; Daniele Regge; Andrea Giovagnoni; Rita Golfieri; Corrado Bibbolino; Vittorio Miele; Emanuele Neri; Roberto Grassi
Journal:  Radiol Med       Date:  2020-04-29       Impact factor: 3.469

Review 2.  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

3.  European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age.

Authors:  Lene Bjerke Laborie; Jaishree Naidoo; Erika Pace; Pierluigi Ciet; Christine Eade; Matthias W Wagner; Thierry A G M Huisman; Susan C Shelmerdine
Journal:  Pediatr Radiol       Date:  2022-06-22

4.  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

5.  Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.

Authors:  Lea Strohm; Charisma Hehakaya; Erik R Ranschaert; Wouter P C Boon; Ellen H M Moors
Journal:  Eur Radiol       Date:  2020-05-26       Impact factor: 5.315

Review 6.  Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease.

Authors:  Nobuyuki Kagiyama; Sirish Shrestha; Peter D Farjo; Partho P Sengupta
Journal:  J Am Heart Assoc       Date:  2019-08-27       Impact factor: 5.501

7.  AI-RADS: An Artificial Intelligence Curriculum for Residents.

Authors:  Alexander L Lindqwister; Saeed Hassanpour; Petra J Lewis; Jessica M Sin
Journal:  Acad Radiol       Date:  2020-10-15       Impact factor: 3.173

8.  Women's attitudes to the use of AI image readers: a case study from a national breast screening programme.

Authors:  Niamh Lennox-Chhugani; Yan Chen; Veronica Pearson; Bernadette Trzcinski; Jonathan James
Journal:  BMJ Health Care Inform       Date:  2021-03

9.  Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review.

Authors:  A P Bhandari; R Liong; J Koppen; S V Murthy; A Lasocki
Journal:  AJNR Am J Neuroradiol       Date:  2020-11-26       Impact factor: 3.825

Review 10.  The Bionic Radiologist: avoiding blurry pictures and providing greater insights.

Authors:  Marc Dewey; Uta Wilkens
Journal:  NPJ Digit Med       Date:  2019-07-09
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