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

被引:135
作者
Waymel, Q. [1 ]
Badr, S. [1 ]
Demondion, X. [1 ,2 ]
Cotten, A. [1 ,2 ]
Jacques, T. [1 ,2 ]
机构
[1] Univ Hosp Lille, Dept Musculoskeletal Radiol, F-59037 Lille, France
[2] Univ Lille, Lille Med Sch, F-59045 Lille, France
关键词
Artificial intelligence (AI); Radiologists; Machine learning; Survey; SEGMENTATION; TISSUE;
D O I
10.1016/j.diii.2019.03.015
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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 Pasde-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. (C) 2019 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:327 / 336
页数:10
相关论文
共 31 条
[1]   Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System [J].
Bal, Mert ;
Amasyali, M. Fatih ;
Sever, Hayri ;
Kose, Guven ;
Demirhan, Ayse .
SCIENTIFIC WORLD JOURNAL, 2014,
[2]   Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach [J].
Bennett, Casey C. ;
Hauser, Kris .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2013, 57 (01) :9-19
[3]   Radiology and artificial intelligence: An opportunity for our specialty [J].
Beregi, J. -P. ;
Zins, M. ;
Masson, J. -P. ;
Cart, P. ;
Bartoli, J. -M. ;
Silberman, B. ;
Boudghene, F. ;
Meder, J. -F. .
DIAGNOSTIC AND INTERVENTIONAL IMAGING, 2018, 99 (11) :677-678
[4]   Artificial intelligence in cancer imaging: Clinical challenges and applications [J].
Bi, Wenya Linda ;
Hosny, Ahmed ;
Schabath, Matthew B. ;
Giger, Maryellen L. ;
Birkbak, Nicolai J. ;
Mehrtash, Alireza ;
Allison, Tavis ;
Arnaout, Omar ;
Abbosh, Christopher ;
Dunn, Ian F. ;
Mak, Raymond H. ;
Tamimi, Rulla M. ;
Tempany, Clare M. ;
Swanton, Charles ;
Hoffmann, Udo ;
Schwartz, Lawrence H. ;
Gillies, Robert J. ;
Huang, Raymond Y. ;
Aerts, Hugo J. W. L. .
CA-A CANCER JOURNAL FOR CLINICIANS, 2019, 69 (02) :127-157
[5]   Radiology: Is its future bright? [J].
Blum, A. ;
Zins, M. .
DIAGNOSTIC AND INTERVENTIONAL IMAGING, 2017, 98 (05) :369-371
[6]  
Bray MA, 2018, METHODS MOL BIOL, V1683, P89, DOI 10.1007/978-1-4939-7357-6_7
[7]   Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods [J].
Burlina, Philippe ;
Billings, Seth ;
Joshi, Neil ;
Albayda, Jemima .
PLOS ONE, 2017, 12 (08)
[8]   Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images [J].
Cheng, Phillip M. ;
Malhi, Harshawn S. .
JOURNAL OF DIGITAL IMAGING, 2017, 30 (02) :234-243
[9]  
Ciompi F, 2017, SCI REP, V19
[10]   The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program [J].
Collado-Mesa, Fernando ;
Alvarez, Edilberto ;
Arheart, Kris .
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2018, 15 (12) :1753-1757