Top

Published in:

01-04-2019 | Artificial Intelligence | Editorial Comment

Artificial intelligence in radiology: who’s afraid of the big bad wolf?

Authors: Benoit Gallix, Jaron Chong

Published in: European Radiology | Issue 4/2019

Abstract

This Editorial comment refers to the article “Medical students’ attitude towards artificial intelligence: a multicenter survey,” Pinto Dos Santos D, et al Eur Radiol 2018.

Key Points

• Medical students are not well informed of the potential consequences of AI in radiology.

• The fundamental principles of AI—as well as its application in medicine—must be taught in medical schools.

• The radiologist specialty must actively reflect on how to validate, approve, and integrate AI algorithms into our clinical practices.

Hinton G (2018) Deep learning-a technology with the potential to transform health care. JAMA 320(11):1101–1102CrossRefPubMed

Ribli D, Horváth A, Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with deep learning. Sci Rep 8(1):4165CrossRefPubMedPubMedCentral

Ting DSW, Cheung CY, Lim G et al (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211–2223CrossRefPubMedPubMedCentral

Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118CrossRefPubMedPubMedCentral

Mukherjee S (2017) A.I. Versus M.D. What happens when diagnosis is automated? The New Yorker Annals of Medicine April 3, 2017 Issue. https://www.newyorker.com/magazine/2017/04/03/ai-versus-md

Pinto Dos Santos D, Giese D, Brodehl S et al (2018) Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. https://doi.org/10.1007/s00330-018-5601-1

Gong B, Nugent JP, Guest W et al (2018) Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: a national survey study. Acad Radiol. https://doi.org/10.1016/j.acra.2018.10.007

Collado-Mesa F, Alvarez E, Arheart K (2018) The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol 15(12):1753–1757

Savadjiev P, Chong J, Dohan A et al (2018) Demystification of AI-driven medical image interpretation: past, present and future. Eur Radiol. https://doi.org/10.1007/s00330-018-5674-x

Title: Artificial intelligence in radiology: who’s afraid of the big bad wolf?
Authors: Benoit Gallix
Jaron Chong
Publication date: 01-04-2019
Publisher: Springer Berlin Heidelberg
Keyword: Artificial Intelligence
Published in: European Radiology / Issue 4/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5995-9

At a glance: The STEP trials

Springer Medicine

Artificial intelligence in radiology: who’s afraid of the big bad wolf?

Abstract

Key Points

At a glance: The STEP trials

Springer Medicine

Abstract

Key Points

Please log in to get access to this content

Other articles of this Issue 4/2019

Microvessel ultrasound of neonatal brain parenchyma: feasibility, reproducibility, and normal imaging features by superb microvascular imaging (SMI)

Histopathological to multiparametric MRI spatial mapping of extended systematic sextant and MR/TRUS-fusion-targeted biopsy of the prostate

Brain microstructural alterations in type 2 diabetes: diffusion kurtosis imaging provides added value to diffusion tensor imaging

Accuracy of automated patient positioning in CT using a 3D camera for body contour detection

Precision and reliability of liver iodine quantification from spectral detector CT: evidence from phantom and patient data

Diagnostic value of electric properties tomography (EPT) for differentiating benign from malignant breast lesions: comparison with standard dynamic contrast-enhanced MRI