From data to delivery: a mini-review on the clinical applications and challenges of artificial intelligence in obstetric anesthesia and analgesia

Background Artificial intelligence (AI) is increasingly transforming anesthesiology and perioperative medicine through predictive analytics, real-time monitoring, and clinical decision support. In obstetrics, AI has shown promise in predicting complications such as preeclampsia and postpartum hemorrhage. However, it's integration into obstetric anesthesiology remains limited due to challenges including dynamic maternal physiology, time-sensitive care, and the need to optimize both maternal and fetal outcomes. This mini-review evaluates current evidence and the translational potential of AI in obstetric anesthesiology. Methods A literature review was performed using PubMed, Google Scholar, and OpenEvidence AI, covering studies published up to April 2026, with emphasis on post-2015 literature. Included studies comprised original research, systematic reviews, and case series published in English. Animal studies and editorials without primary data were excluded. Two authors independently screened articles, resolving disagreements by consensus. Discussion Current applications include machine learning-assisted ultrasound guidance for neuraxial procedures, optimization of intrathecal anesthetic dosing, and prediction of spinal anesthesia-induced hypotension. AI models have also been developed to predict epidural-related maternal fever and post-cesarean pain. Despite promising findings, most studies are retrospective and lack external validation. Major barriers include data heterogeneity, algorithmic bias, limited interpretability, and challenges integrating AI into clinical workflows. Conclusion AI has significant potential to improve risk stratification, procedural precision, and personalized care in obstetric anesthesiology. Future research should focus on prospective validation, multicenter collaboration, explainable models, and seamless clinical integration, with AI serving as a support tool rather than a replacement for clinician expertise.