The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Artificial intelligence in the interpretation of breast cancer on MRI
Abstract
Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer‐aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image‐specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI‐based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient‐specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1310–1324.
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Cites
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“These human-engineered radiomic approaches were later augmented with deep-learning approaches.(56,57) FIGURE 3: Illustration of the first publication of a CNN (aka shift-invariant neural network) used in medical image analysis.”
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Benjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century
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Phenomic studies on diseases: potential and challenges
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Are physicians and medical students ready for artificial intelligence applications in healthcare?
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Metadata
- Authors
- Sheth, D., & Giger, M. L.
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2019-07-25
- Volume
- 51
- Issue
- 5
- Pages
- 1310-1324
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- OpenAlex
- W2963716858
- Cited by
- 219
- References
- 8
- FWCI
- 12.15
- Type
- journal article
- Language
- en
- Open access
- closed
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