Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications

Abstract

PURPOSE OF REVIEW: Maternal morbidity and mortality remain largely preventable, yet current risk-assessment tools identify only a fraction of women who experience severe complications. This review synthesizes recent advances in artificial intelligence and machine learning for early prediction, decision support, and procedural guidance in obstetric anesthesia, with a focus on postpartum hemorrhage, hypertensive disease, sepsis, hemodynamic instability, neuraxial procedures, and peripartum pain. RECENT FINDINGS: Recently, electronic health record (EHR)-integrated and imaging-based machine learning models have outperformed traditional risk scores for postpartum hemorrhage, placenta accreta spectrum, and pre-eclampsia, and are beginning to incorporate multiomics and genetic data. Obstetric-specific early warning systems and parsimonious machine learning models for maternal sepsis and epidural-related fever show promise but remain limited by sensitivity and external validation. Waveform analytics, noninvasive hemodynamic indices, and artificial intelligence-assisted ultrasound can anticipate hypotension and enhance neuraxial and regional block placement. Machine learning frameworks for postcesarean and chronic postpartum pain, together with virtual reality interventions, support more individualized analgesia. SUMMARY: Artificial intelligence-enabled tools are poised to augment, rather than replace, clinician judgment in obstetric anesthesia. Real-world impact will depend on rigorous external validation, equitable implementation, interpretable model design, seamless EHR integration, and close collaboration between clinicians, data scientists, and vendors.

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