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Natural Language Processing for Breast Imaging: A Systematic Review.

Kareem Mahmoud Diab1, Jamie Deng2, Yusen Wu1

  • 1Institute for Data Science and Computing, University of Miami, Miami, FL 33146, USA.

Diagnostics (Basel, Switzerland)
|May 16, 2023
PubMed
Summary
This summary is machine-generated.

Natural Language Processing (NLP) enhances breast imaging by improving triage, diagnosis, and characterization. This review covers NLP techniques and applications for better breast cancer care.

Keywords:
Natural Language Processingbreast cancerbreast imagingpathology reportsradiology reportssystematic review

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Area of Science:

  • Radiology
  • Medical Informatics
  • Natural Language Processing

Background:

  • Natural Language Processing (NLP) is increasingly vital in diagnostic radiology.
  • Its application in breast imaging can significantly improve patient care pathways.

Purpose of the Study:

  • To provide a comprehensive review of recent advancements in NLP for breast imaging.
  • To cover key techniques, applications, and future opportunities in this field.

Main Methods:

  • Review of current literature on NLP techniques applied to breast imaging data.
  • Analysis of methods for information extraction from clinical notes, radiology reports, and pathology reports.
  • Evaluation of NLP-based decision support systems in breast imaging.

Main Results:

  • NLP methods effectively extract crucial information from various medical reports.
  • NLP applications show potential in enhancing the accuracy and efficiency of breast imaging diagnostics.
  • Current NLP-based decision support systems offer promising capabilities.

Conclusions:

  • NLP holds significant potential for advancing breast imaging care.
  • Further research and development are needed to overcome challenges and maximize NLP's impact.
  • This review offers insights for clinicians and researchers in this rapidly evolving domain.