Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Radiology

Background:

• Artificial intelligence (AI) is rapidly advancing in breast imaging.
• Despite progress, widespread clinical adoption is limited.
• Unanswered questions hinder AI integration into daily practice.

Purpose of the Study:

• To provide an overview of key limitations hindering AI implementation in breast imaging.
• To highlight challenges for successful clinical integration of AI tools.
• To identify areas requiring attention for AI in breast radiology.

Main Methods:

• Literature review of AI in breast imaging.
• Analysis of current challenges and limitations.
• Discussion of data, regulatory, privacy, bias, and ethical concerns.

Main Results:

• Limited FDA-approved AI algorithms and annotated datasets.
• Concerns regarding data privacy, security, transparency, and bias.
• Ethical considerations impacting AI deployment.

Conclusions:

• Successful AI implementation requires addressing data, regulatory, and ethical challenges.
• Transparency in AI workflows and performance metrics is essential.
• Community-wide sharing of AI implementation experiences is vital for progress.