Natural Language Processing for Breast Imaging: A Systematic Review

Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of re...

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Bibliographic Details
Published in:Diagnostics (Basel) 2023-04, Vol.13 (8), p.1420
Main Authors: Diab, Kareem Mahmoud, Deng, Jamie, Wu, Yusen, Yesha, Yelena, Collado-Mesa, Fernando, Nguyen, Phuong
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Breast cancer
breast imaging
Business metrics
Computational linguistics
Deep learning
Diagnosis
Language
Language processing
Machine learning
Magnetic resonance imaging
Mammography
Medical screening
Natural language interfaces
Natural Language Processing
Neural networks
pathology reports
R&D
Radiology
radiology reports
Radiology, Medical
Research & development
Review
Semantics
Systematic review
Technology application
Ultrasonic imaging
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Summary:Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13081420