Artificial Intelligence in Obstetrics: Current Applications, Opportunities, and Clinical Implementation Challenges.
Authors
Affiliations (4)
Affiliations (4)
- Department of Obstetrics and Perinatal Medicine, Goethe University Frankfurt and University Hospital Frankfurt am Main, Germany.
- Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, City St George's University of London, London, United Kingdom.
- Fetal Medicine Unit, St George's University Hospital, London, United Kingdom.
- Fetal Medicine Unit, Liverpool Women's Hospital, Liverpool, United Kingdom.
Abstract
Artificial intelligence is transforming obstetric practice through applications in diagnostic imaging, risk prediction, and clinical decision-making. Deep learning algorithms have achieved diagnostic accuracy comparable to that of experienced clinicians. However, gaps persist between algorithmic capability and clinical implementation. Critical challenges include limited external validation and algorithmic bias. This review examines current AI applications in obstetrics across multiple clinical domains: automated fetal biometry, structural anomaly detection, prediction of pregnancy complications, and intrapartum fetal surveillance. It highlights persistent technical, ethical, and implementation barriers. Key recommendations include multicenter validation across diverse populations, explainable AI approaches, and creating strong regulatory frameworks.