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Artificial Intelligence in Gynecologic Imaging.

Morgan Briggs1, Ayesha Saif1, Timothy L Kline2

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|January 23, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can aid in imaging for uterine fibroids, endometriosis, and adenomyosis. While promising for diagnosis and prediction, further multi-institutional studies are needed to validate AI tools in clinical practice.

Keywords:
artificial intelligenceendometriosisfibroidsmachine-learningpelvic imaging

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Area of Science:

  • Gynecologic imaging
  • Artificial intelligence in medicine
  • Medical diagnostics

Background:

  • Uterine fibroids, endometriosis, and adenomyosis are common gynecologic conditions.
  • Accurate imaging is crucial for diagnosis and management.
  • AI offers potential to enhance diagnostic capabilities.

Purpose of the Study:

  • To review current artificial intelligence (AI) applications in the imaging of uterine fibroids, endometriosis, and adenomyosis.
  • To assess the capabilities of AI in image recognition, segmentation, localization, and differentiation.
  • To evaluate AI's role in diagnosis and fertility impact prediction.

Main Methods:

  • Systematic review of AI applications in gynecologic imaging.
  • Analysis of AI model performance in identifying and characterizing uterine pathologies.
  • Evaluation of AI's utility in predicting endometriosis-related fertility outcomes.

Main Results:

  • AI models demonstrate proficiency in recognizing, segmenting, and localizing uterine fibroids.
  • AI can assist in differentiating benign fibroids from sarcomas.
  • AI aids in diagnosing adenomyosis and endometriosis, and predicting endometriosis's effect on fertility.

Conclusions:

  • AI tools have the potential to improve objectivity, reduce variability, and shorten interpretation times in gynecologic imaging.
  • Current research is limited by single-institution designs and reliance on expert interpretation.
  • Further robust, multi-institutional validation is required for widespread AI adoption in clinical practice.