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Artificial Intelligence in Breast X-Ray Imaging.

Srinivasan Vedantham1, Mohammed Salman Shazeeb2, Alan Chiang1

  • 1Department of Medical Imaging, University of Arizona, Tucson, AZ.

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Artificial intelligence (AI) shows promise in breast cancer screening by aiding detection, diagnosis, and risk assessment. Further prospective studies are needed to confirm real-world efficacy and address ethical considerations before widespread clinical adoption.

Keywords:
Artificial intelligenceBreast cancerMammographyTomosynthesisdeep learning

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is rapidly advancing, with significant potential applications in clinical breast x-ray imaging.
  • Current AI applications in breast imaging include risk estimation, screening triage, and enhancing detection and diagnosis.
  • AI tools may improve sensitivity and specificity while reducing radiologists' reading time.

Purpose of the Study:

  • To review the clinical applications of artificial intelligence (AI) in breast x-ray imaging.
  • To highlight the potential benefits of AI in breast cancer screening and diagnosis.
  • To identify the need for prospective studies and address implementation challenges.

Main Methods:

  • This is a topical review of existing literature on AI in breast imaging.
  • The review synthesizes findings from numerous retrospective studies.
  • It discusses the potential roles of AI in mammography and digital breast tomosynthesis interpretation.

Main Results:

  • AI demonstrates potential in breast cancer risk estimation, screening triage, and improving diagnostic accuracy.
  • AI can function as a supplementary tool for radiologists, potentially acting as a second reader.
  • Most current evidence is derived from retrospective studies, indicating a need for further research.

Conclusions:

  • AI holds considerable promise for revolutionizing breast cancer screening and diagnosis.
  • Prospective clinical studies are crucial to validate AI's real-world efficacy and clinical utility.
  • Ethical, medicolegal, and liability issues must be carefully considered before routine AI implementation in breast imaging clinics.