Federated learning in obstetrics and gynaecology: A systematic review of applications, architectures, privacy frameworks, and clinical translation challenges
Abstract
Obstetric and gynaecological records are among the most privacy-sensitive categories of personal health data. Conditions such as cervical cancer, preeclampsia, gestational diabetes mellitus, and preterm birth contribute substantially to maternal and perinatal morbidity and mortality, particularly in low-resource settings. Federated learning (FL) enables collaborative model training while raw patient records remain within institutional boundaries, making it a promising approach for privacy-preserving artificial intelligence (AI) in women’s health. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and synthesises FL research in obstetrics and gynaecology (OB/GYN), characterising clinical applications, architectures, privacy-enhancing technologies (PETs), model performance, methodological quality, and clinical translation barriers. Eight electronic databases were searched from inception to March 2026. Eligible studies applied a genuine federated, split, swarm, or privacy-preserving decentralised learning architecture to an obstetric or gynaecological task and reported at least one quantitative performance metric. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Quantitative synthesis used descriptive subgroup area under the receiver operating characteristic curve…
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