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

Morris, 2017, A remarkable resurgence of artificial intelligence and its impact on automation and autonomy, IEEE Trans Autom Sci Eng, 14, 407, 10.1109/TASE.2016.2640778

Hinton, 2018, Deep learning: a technology with the potential to transform health care, JAMA, 320, 10.1001/jama.2018.11100

Pesapane, 2018, Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine, Eur Radiol Exp, 2, 10.1186/s41747-018-0061-6

van Ginneken, 2019, Deep learning for triage of chest radiographs: should every institution train its own system?, Radiology, 290, 545, 10.1148/radiol.2018182318

Bal, 2014, Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system, Scientific World Journal, 2014, 10.1155/2014/137896

Bennett, 2013, Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach, Artif Intell Med, 57, 9, 10.1016/j.artmed.2012.12.003

Ciompi, 2017, Towards automatic pulmonary nodule management in lung cancer screening with deep learning, Sci Rep, 19

Nibali, 2017, Pulmonary nodule classification with deep residual networks, Int J Comput Assist Radiol Surg, 12, 1799, 10.1007/s11548-017-1605-6

Herweh, 2016, Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients, Int J Stroke, 11, 438, 10.1177/1747493016632244

Pedoia, 2016, Segmentation of joint and musculoskeletal tissue in the study of arthritis, MAGMA, 29, 207, 10.1007/s10334-016-0532-9

Polan, 2016, Tissue segmentation of computed tomography images using a random forest algorithm: a feasibility study, Phys Med Biol, 61, 6553, 10.1088/0031-9155/61/17/6553

Xiong, 2019, Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network, IEEE Trans Med Imaging, 38, 515, 10.1109/TMI.2018.2866845

Wu, 2017, Iterative low-dose CT reconstruction with priors trained by artificial neural network, IEEE Trans Med Imaging, 36, 2479, 10.1109/TMI.2017.2753138

Bray, 2018, Quality control for high-throughput imaging experiments using machine learning in cellprofiler, Methods Mol Biol, 1683, 89, 10.1007/978-1-4939-7357-6_7

Sevenster, 2015, Natural language processing techniques for extracting and categorizing finding measurements in narrative radiology reports, Appl Clin Inform, 6, 600, 10.4338/ACI-2014-11-RA-0110

Moehrle, 2018, Radiology” is going away, And that's okay: titles change, a profession evolves. J Am Coll Radiol, 15, 499

Schier, 2018, Artificial intelligence and the practice of radiology: an alternative view, J Am Coll Radiol, 15, 1004, 10.1016/j.jacr.2018.03.046

Blum, 2017, Radiology: is its future bright?, Diagn Interv Imaging, 98, 369, 10.1016/j.diii.2017.04.002

Tang, 2018, Canadian association of radiologists white paper on artificial intelligence in radiology, Can Assoc Radiol J, 69, 120, 10.1016/j.carj.2018.02.002

Beregi, 2018, Radiology and artificial intelligence: an opportunity for our specialty, Diagn Interv Imaging, 99, 677, 10.1016/j.diii.2018.11.002

Pinto Dos Santos, 2019, Medical students’ attitude towards artificial intelligence: a multicentre survey, Eur Radiol, 29, 1640, 10.1007/s00330-018-5601-1

Gong, 2018, Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study, Acad Radiol, 26, 566, 10.1016/j.acra.2018.10.007

Collado-Mesa, 2018, The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program, J Am Coll Radiol, 15, 1753, 10.1016/j.jacr.2017.12.021

Lincoln, 2019, Augmented radiology: looking over the horizon, Radiology: Artificial Intelligence, 1

DREES. Effectifs des médecins par spécialité, zone d’inscription et tranche d’âge. http://www.data.drees.sante.gouv.fr/TableViewer/tableView.aspx?ReportId=3803. (accessed on March 29, 2019).

Gallix, 2019, Artificial intelligence in radiology: who's afraid of the big bad wolf?, Eur Radiol, 29, 1637, 10.1007/s00330-018-5995-9

College des Enseignants en Radiologie de France. Programme d’Enseignements 2018-2019.https://cerf.radiologie.fr/sites/cerf.radiologie.fr/files/Enseignement/DES/Calendriers/Calendrier%202018-2019%20V8.pdf. (accessed on March 29, 2019).

Bi, 2019, Artificial intelligence in cancer imaging: clinical challenges and applications, CA: Cancer J Clin, 69, 127

Cheng, 2017, Transfer learning with convolutional neural networks for classification of abdominal ultrasound images, J Digit Imaging, 30, 234, 10.1007/s10278-016-9929-2

Burlina, 2017, Automated diagnosis of myositis from muscle ultrasound: exploring the use of machine learning and deep learning methods, PLoS One, 12, 10.1371/journal.pone.0184059

Huang, 2018, Machine learning in ultrasound computer-aided diagnostic systems: a survey, Biomed Res Int, 2018, 10.1155/2018/5137904

Letzen, 2019, The role of artificial intelligence in interventional oncology: a primer, J Vasc Interv Radiol, 30, 38, 10.1016/j.jvir.2018.08.032

Ambroise Grandjean, 2018, Artificial intelligence assistance for fetal head biometry: assessment of automated measurement software, Diagn Interv Imaging, 99, 709, 10.1016/j.diii.2018.08.001