Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology
- PMID: 29502585
- DOI: 10.1016/j.jacr.2018.01.028
Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology
Abstract
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era.
Keywords: Diagnostic decision support; artificial intelligence; cognitive assistants; deep learning; knowledge and reasoning; machine learning; medical image analysis.
Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.
Similar articles
-
Machine Learning in Medical Imaging.J Am Coll Radiol. 2018 Mar;15(3 Pt B):512-520. doi: 10.1016/j.jacr.2017.12.028. Epub 2018 Feb 2. J Am Coll Radiol. 2018. PMID: 29398494 Review.
-
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.J Am Coll Radiol. 2018 Mar;15(3 Pt B):504-508. doi: 10.1016/j.jacr.2017.12.026. Epub 2018 Feb 4. J Am Coll Radiol. 2018. PMID: 29402533
-
Current Applications and Future Impact of Machine Learning in Radiology.Radiology. 2018 Aug;288(2):318-328. doi: 10.1148/radiol.2018171820. Epub 2018 Jun 26. Radiology. 2018. PMID: 29944078 Free PMC article. Review.
-
Machine Learning in Radiology: Applications Beyond Image Interpretation.J Am Coll Radiol. 2018 Feb;15(2):350-359. doi: 10.1016/j.jacr.2017.09.044. Epub 2017 Nov 17. J Am Coll Radiol. 2018. PMID: 29158061
-
Interpretable Artificial Intelligence: Why and When.AJR Am J Roentgenol. 2020 May;214(5):1137-1138. doi: 10.2214/AJR.19.22145. Epub 2020 Mar 4. AJR Am J Roentgenol. 2020. PMID: 32130042
Cited by
-
Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning.Entropy (Basel). 2021 Mar 24;23(4):382. doi: 10.3390/e23040382. Entropy (Basel). 2021. PMID: 33804831 Free PMC article.
-
Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings.Sci Rep. 2021 Mar 9;11(1):5529. doi: 10.1038/s41598-021-85016-9. Sci Rep. 2021. PMID: 33750857 Free PMC article.
-
Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging.Diagnostics (Basel). 2023 Nov 18;13(22):3471. doi: 10.3390/diagnostics13223471. Diagnostics (Basel). 2023. PMID: 37998607 Free PMC article.
-
Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.Sci Rep. 2023 Jul 25;13(1):12018. doi: 10.1038/s41598-023-38586-9. Sci Rep. 2023. PMID: 37491504 Free PMC article.
-
Medical students' attitude towards artificial intelligence: a multicentre survey.Eur Radiol. 2019 Apr;29(4):1640-1646. doi: 10.1007/s00330-018-5601-1. Epub 2018 Jul 6. Eur Radiol. 2019. PMID: 29980928
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources