Literature DB >> 31492409

Artificial Intelligence in Imaging: The Radiologist's Role.

Daniel L Rubin1.   

Abstract

Rapid technological advancements in artificial intelligence (AI) methods have fueled explosive growth in decision tools being marketed by a rapidly growing number of companies. AI developments are being driven largely by computer scientists, informaticians, engineers, and businesspeople, with much less direct participation by radiologists. Participation by radiologists in AI is largely restricted to educational efforts to familiarize them with the tools and promising results, but techniques to help them decide which AI tools should be used in their practices and to how to quantify their value are not being addressed. This article focuses on the role of radiologists in imaging AI and suggests specific ways they can be engaged by (1) considering the clinical need for AI tools in specific clinical use cases, (2) undertaking formal evaluation of AI tools they are considering adopting in their practices, and (3) maintaining their expertise and guarding against the pitfalls of overreliance on technology.
Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; evaluation; imaging; radiology

Mesh:

Year:  2019        PMID: 31492409      PMCID: PMC6733578          DOI: 10.1016/j.jacr.2019.05.036

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  44 in total

1.  Interpretation time of computer-aided detection at screening mammography.

Authors:  Philip M Tchou; Tamara Miner Haygood; E Neely Atkinson; Tanya W Stephens; Paul L Davis; Elsa M Arribas; William R Geiser; Gary J Whitman
Journal:  Radiology       Date:  2010-08-02       Impact factor: 11.105

2.  Accuracy of screening mammography interpretation by characteristics of radiologists.

Authors:  William E Barlow; Chen Chi; Patricia A Carney; Stephen H Taplin; Carl D'Orsi; Gary Cutter; R Edward Hendrick; Joann G Elmore
Journal:  J Natl Cancer Inst       Date:  2004-12-15       Impact factor: 13.506

3.  Influence of computer-aided detection on performance of screening mammography.

Authors:  Joshua J Fenton; Stephen H Taplin; Patricia A Carney; Linn Abraham; Edward A Sickles; Carl D'Orsi; Eric A Berns; Gary Cutter; R Edward Hendrick; William E Barlow; Joann G Elmore
Journal:  N Engl J Med       Date:  2007-04-05       Impact factor: 91.245

4.  Effectiveness of computer-aided detection in community mammography practice.

Authors:  Joshua J Fenton; Linn Abraham; Stephen H Taplin; Berta M Geller; Patricia A Carney; Carl D'Orsi; Joann G Elmore; William E Barlow
Journal:  J Natl Cancer Inst       Date:  2011-07-27       Impact factor: 13.506

5.  Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees.

Authors:  Joshua J Fenton; Guibo Xing; Joann G Elmore; Heejung Bang; Steven L Chen; Karen K Lindfors; Laura-Mae Baldwin
Journal:  Ann Intern Med       Date:  2013-04-16       Impact factor: 25.391

6.  Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography.

Authors:  Eugenio Alberdi; Andrey Povykalo; Lorenzo Strigini; Peter Ayton
Journal:  Acad Radiol       Date:  2004-08       Impact factor: 3.173

7.  Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

Authors:  Puja Bharti; Deepti Mittal; Rupa Ananthasivan
Journal:  Ultrason Imaging       Date:  2016-08-02       Impact factor: 1.578

8.  Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection.

Authors:  Constance D Lehman; Robert D Wellman; Diana S M Buist; Karla Kerlikowske; Anna N A Tosteson; Diana L Miglioretti
Journal:  JAMA Intern Med       Date:  2015-11       Impact factor: 21.873

9.  Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

Authors:  Wei Li; Peng Cao; Dazhe Zhao; Junbo Wang
Journal:  Comput Math Methods Med       Date:  2016-12-14       Impact factor: 2.238

10.  Measures of Diagnostic Accuracy: Basic Definitions.

Authors:  Ana-Maria Šimundić
Journal:  EJIFCC       Date:  2009-01-20
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  15 in total

1.  Natural Language Processing of Radiology Text Reports: Interactive Text Classification.

Authors:  Walter F Wiggins; Felipe Kitamura; Igor Santos; Luciano M Prevedello
Journal:  Radiol Artif Intell       Date:  2021-05-12

2.  Artificial Intelligence in Thoracic Radiology. A Challenge in COVID-19 Times?

Authors:  María Dolores Corbacho Abelaira; Alberto Ruano-Ravina; Alberto Fernández-Villar
Journal:  Arch Bronconeumol       Date:  2020-10-22       Impact factor: 4.872

Review 3.  Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.

Authors:  Jean Feng; Rachael V Phillips; Ivana Malenica; Andrew Bishara; Alan E Hubbard; Leo A Celi; Romain Pirracchio
Journal:  NPJ Digit Med       Date:  2022-05-31

4.  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 5.  Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.

Authors:  Eui Jin Hwang; Chang Min Park
Journal:  Korean J Radiol       Date:  2020-05       Impact factor: 3.500

6.  Radiologist observations of computed tomography (CT) images predict treatment outcome in TB Portals, a real-world database of tuberculosis (TB) cases.

Authors:  Gabriel Rosenfeld; Andrei Gabrielian; Qinlu Wang; Jingwen Gu; Darrell E Hurt; Alyssa Long; Alex Rosenthal
Journal:  PLoS One       Date:  2021-03-17       Impact factor: 3.240

7.  AI Lung Segmentation and Perfusion Analysis of Dual-Energy CT Can Help to Distinguish COVID-19 Infiltrates from Visually Similar Immunotherapy-Related Pneumonitis Findings and Can Optimize Radiological Workflows.

Authors:  Andreas S Brendlin; Markus Mader; Sebastian Faby; Bernhard Schmidt; Ahmed E Othman; Sebastian Gassenmaier; Konstantin Nikolaou; Saif Afat
Journal:  Tomography       Date:  2021-12-23

8.  Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies.

Authors:  Almut Kundisch; Alexander Hönning; Sven Mutze; Lutz Kreissl; Frederik Spohn; Johannes Lemcke; Maximilian Sitz; Paul Sparenberg; Leonie Goelz
Journal:  PLoS One       Date:  2021-11-29       Impact factor: 3.240

Review 9.  Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.

Authors:  Alessandro Allegra; Alessandro Tonacci; Raffaele Sciaccotta; Sara Genovese; Caterina Musolino; Giovanni Pioggia; Sebastiano Gangemi
Journal:  Cancers (Basel)       Date:  2022-01-25       Impact factor: 6.639

Review 10.  Reviewing the relationship between machines and radiology: the application of artificial intelligence.

Authors:  Rani Ahmad
Journal:  Acta Radiol Open       Date:  2021-02-09
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