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Artificial Intelligence in Obstetrics: Current Applications, Opportunities, and Clinical Implementation Challenges.

Eileen Deuster1, Asma Khalil2,3,4

  • 1Department of Obstetrics and Perinatal Medicine, Goethe University Frankfurt and University Hospital Frankfurt am Main, Germany.

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Summary

Artificial intelligence (AI) shows promise in obstetrics, matching clinician accuracy in some tasks. However, challenges like bias and validation hinder real-world use, requiring diverse testing and clear regulations.

Keywords:
anomaly detectionartificial intelligencedeep learningfetal biometrymachine learningpreeclampsia prediction

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Area of Science:

  • Obstetrics and Gynecology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly applied in obstetrics, enhancing diagnostic imaging, risk prediction, and clinical decision-making.
  • Deep learning algorithms demonstrate diagnostic accuracy rivaling experienced clinicians in specific obstetric applications.

Purpose of the Study:

  • To review current AI applications in obstetrics, covering fetal biometry, anomaly detection, complication prediction, and intrapartum surveillance.
  • To identify persistent technical, ethical, and implementation barriers to AI adoption in clinical obstetric practice.

Main Methods:

  • Literature review of AI applications in obstetrics.
  • Analysis of AI performance in fetal biometry, anomaly detection, risk prediction, and fetal surveillance.
  • Examination of challenges including external validation, algorithmic bias, and implementation hurdles.

Main Results:

  • AI demonstrates high accuracy in automated fetal biometry and structural anomaly detection.
  • AI shows potential in predicting pregnancy complications and intrapartum fetal surveillance.
  • Significant challenges remain, including limited external validation across diverse populations and inherent algorithmic bias.

Conclusions:

  • AI holds transformative potential for obstetric practice but faces critical implementation gaps.
  • Addressing challenges requires multicenter validation, explainable AI (XAI) methods, and robust regulatory frameworks.
  • Future efforts must focus on ensuring equitable and reliable AI integration into routine obstetric care.