ISSN: 1899-0967
Polish Journal of Radiology
Established by prof. Zygmunt Grudziński in 1926 Sun
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1/2022
vol. 87
 
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Interventional radiology
abstract:
Review paper

Applications and challenges of artificial intelligence in diagnostic and interventional radiology

Joseph Waller
1
,
Aisling O’Connor
2
,
Eleeza Raafat
3
,
Ahmad Amireh
4
,
John Dempsey
5
,
Clarissa Martin
6
,
Muhammad Umair
7

1.
Drexel University College of Medicine, USA
2.
University of Washington, Seattle, WA 98195, USA
3.
Loyola University Chicago Stritch School of Medicine, Maywood, IL 60153, USA
4.
Duke University, Department of Biology, Durham, NC 27708, USA
5.
Boston College, Chestnut Hill, MA 02467, USA
6.
University of Pennsylvania, Philadelphia PA 17101, USA
7.
Northwestern University Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
Pol J Radiol 2022; 87: e113-e117
Online publish date: 2022/02/25
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Purpose
Machine learning (ML) and deep learning (DL) can be utilized in radiology to help diagnosis and for predicting management and outcomes based on certain image findings. DL utilizes convolutional neural networks (CNN) and may be used to classify imaging features. The objective of this literature review is to summarize recent publications highlighting the key ways in which ML and DL may be applied in radiology, along with solutions to the problems that this implementation may face.

Material and methods
Twenty-one publications were selected from the primary literature through a PubMed search. The articles included in our review studied a range of applications of artificial intelligence in radiology.

Results
The implementation of artificial intelligence in diagnostic and interventional radiology may improve image analysis, aid in diagnosis, as well as suggest appropriate interventions, clinical predictive modelling, and trainee education. Potential challenges include ethical concerns and the need for appropriate datasets with accurate labels and large sample sizes to train from. Additionally, the training data should be representative of the population to which the future ML platform will be applicable. Finally, machines do not disclose a statistical rationale when expounding on the task purpose, making them difficult to apply in medical imaging.

Conclusions
As radiologists report increased workload, utilization of artificial intelligence may provide improved outcomes in medical imaging by assisting, rather than guiding or replacing, radiologists. Further research should be done on the risks of AI implementation and how to most accurately validate the results.

keywords:

artificial intelligence, machine learning, radiology, imaging




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