Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions

Background: The application of artificial intelligence (AI) in medicine has advanced significantly, particularly in obstetrics, where it plays an increasingly prominent role in predicting modes of delivery and assessment of maternal risks. AI-assisted prediction of delivery modes, a cutting-edge field at the intersection of medicine and computer science, aims to support clinicians in making more accurate and safer delivery decisions by utilizing advanced AI technologies and big data analytics. With increasing individual variability among pregnant women, traditional clinical experience is often insufficient to meet the requirements of personalized medicine; therefore, establishing a scientific prediction model is particularly crucial. This systematic review aims to evaluate the current state of research on AI-assisted prediction of delivery modes, compare AI predictions and traditional statistical methods, and propose future research directions. Methods: A comprehensive literature search was conducted in the PubMed, Web of Science, and ScienceDirect databases, encompassing publications up to November 2024. Results: Analysis of existing studies demonstrates that AI models outperform conventional statistical methods in predicting delivery modes, highlighting their potential as valuable tools in obstetric diagnosis and clinical decision-making. However, several critical limitations persist in current research, including: (a) the absence of real-time decision support during dynamic labor progression; (b) insufficient multi-center collaboration and a lack of external validation frameworks; and (c) inadequate standardization of clinical parameters (e.g. inconsistent definitions of cervical dilation thresholds and fetal descent metrics). These methodological gaps limit the clinical applicability and generalizability of AI-driven predictive systems across diverse obstetric populations and care settings. Conclusions: Future research should prioritize data standardization and sharing, enhance the generalizability of prediction models, address ethical considerations, and ensure the fairness and transparency of AI algorithms to improve clinical trust and applicability. Registration: The study has been registered on https://www.crd.york.ac.uk/prospero/ (registration number: CRD420251068005).