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Clinical/methodical issue <p>Artificial intelligence (AI) is being increasingly integrated into clinical practice. However, the specific benefits are still unclear to many users.</p> Standard radiological methods <p>In principle, AI applications are available for all imaging modalities, with a&#xa0;particular focus on mammography in breast diagnostics.</p> Methodical innovations <p>AI promises to filter examinations into <i>negative</i> and <i>clearly positive</i> findings, and thereby reduces part of the radiological workload. Other applications are not yet as widely established.</p> Performance <p>AI methods for mammography, and to a&#xa0;lesser extent tomosynthesis, have already reached the diagnostic quality of radiologists.</p> Achievements <p>Except for second-opinion applications/triage in mammography, most methods are still under development.</p> Practical recommendations <p>Currently, most AI applications must be critically evaluated by potential users regarding their maturity and practical benefits.</p>

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Künstliche Intelligenz in der Mammadiagnostik

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月6日

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  • Matthias Dietzel,
  • Alexandra Resch,
  • Pascal A. T. Baltzer

摘要

Clinical/methodical issue

Artificial intelligence (AI) is being increasingly integrated into clinical practice. However, the specific benefits are still unclear to many users.

Standard radiological methods

In principle, AI applications are available for all imaging modalities, with a particular focus on mammography in breast diagnostics.

Methodical innovations

AI promises to filter examinations into negative and clearly positive findings, and thereby reduces part of the radiological workload. Other applications are not yet as widely established.

Performance

AI methods for mammography, and to a lesser extent tomosynthesis, have already reached the diagnostic quality of radiologists.

Achievements

Except for second-opinion applications/triage in mammography, most methods are still under development.

Practical recommendations

Currently, most AI applications must be critically evaluated by potential users regarding their maturity and practical benefits.