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Abstract

Given the enormous output and pace of development of artificial intelligence (AI) methods in medical imaging, it can be challenging to identify the true success stories to determine the state-of-the-art of the field. This report seeks to provide the magnetic resonance imaging (MRI) community with an initial guide into the major areas in which the methods of AI are contributing to MRI in oncology. After a general introduction to artificial intelligence, we proceed to discuss the successes and current limitations of AI in MRI when used for image acquisition, reconstruction, registration, and segmentation, as well as its utility for assisting in diagnostic and prognostic settings. Within each section, we attempt to present a balanced summary by first presenting common techniques, state of readiness, current clinical needs, and barriers to practical deployment in the clinical setting. We conclude by presenting areas in which new advances must be realized to address questions regarding generalizability, quality assurance and control, and uncertainty quantification when applying MRI to cancer to maintain patient safety and practical utility.

Details

Title
A critical assessment of artificial intelligence in magnetic resonance imaging of cancer
Author
Wu, Chengyue 1 ; Andaloussi, Meryem Abbad 2 ; Hormuth, David A. 3 ; Lima, Ernesto A. B. F. 4 ; Lorenzo, Guillermo 5 ; Stowers, Casey E. 6 ; Ravula, Sriram 7 ; Levac, Brett 7 ; Dimakis, Alexandros G. 7 ; Tamir, Jonathan I. 8 ; Brock, Kristy K. 9 ; Chung, Caroline 10 ; Yankeelov, Thomas E. 11 

 The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Department of Breast Imaging, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Department of Biostatistics, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Institute for Data Science in Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
 Technology and Medicine University of Luxembourg, Faculty of Science, Belvaux, Luxembourg (GRID:grid.16008.3f) (ISNI:0000 0001 2295 9843) 
 The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Livestrong Cancer Institutes, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
 The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Texas Advanced Computing Center, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
 The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); Health Research Institute of Santiago de Compostela, Santiago de Compostela, Spain (GRID:grid.488911.d) (ISNI:0000 0004 0408 4897) 
 The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
 The University of Texas at Austin, Chandra Family Department of Electrical and Computer Engineering, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
 The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Chandra Family Department of Electrical and Computer Engineering, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Department of Diagnostic Medicine, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
 The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Institute for Data Science in Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
10  The University of Texas MD Anderson Cancer Center, Institute for Data Science in Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas MD Anderson Cancer Center, Department of Neuroradiology, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776) 
11  The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, USA (GRID:grid.240145.6) (ISNI:0000 0001 2291 4776); The University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Livestrong Cancer Institutes, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Department of Diagnostic Medicine, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Department of Biomedical Engineering, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924); The University of Texas at Austin, Department of Oncology, Austin, USA (GRID:grid.89336.37) (ISNI:0000 0004 1936 9924) 
Pages
15
Publication year
2025
Publication date
Dec 2025
Publisher
Nature Publishing Group
e-ISSN
2948197X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3225864201
Copyright
Copyright Nature Publishing Group Dec 2025