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Artificial Intelligence in Clinical Practice: Implementation Considerations and Barriers.

Manisha Bahl1

  • 1Massachusetts General Hospital, Department of Radiology, Boston, MA, USA.

Journal of Breast Imaging
|December 19, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers benefits for breast imaging, but clinical implementation requires careful steps. High costs and liability concerns are key barriers to widespread adoption of AI tools.

Keywords:
artificial intelligencebreast imagingimplementationmammography

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Over 20 artificial intelligence (AI) algorithms for breast imaging have received Food and Drug Administration clearance.
  • AI in radiology is rapidly growing, impacting diagnostic and screening processes.

Purpose of the Study:

  • To list available AI products for breast imaging.
  • To describe key elements for clinical implementation of AI.
  • To discuss barriers hindering AI adoption in breast imaging.

Main Methods:

  • Review of current AI products for breast imaging.
  • Outline of the clinical implementation process for AI tools.
  • Analysis of challenges associated with AI integration.

Main Results:

  • Multiple AI algorithms are cleared for breast imaging applications.
  • Clinical implementation involves stakeholder identification, product selection, local evaluation, workflow integration, and performance monitoring.
  • Significant barriers include high costs and liability concerns.

Conclusions:

  • Successful AI implementation in breast imaging requires a structured approach.
  • Addressing cost and liability issues is crucial for broader AI adoption.
  • AI holds potential for enhancing quality and efficiency in breast imaging.