Anesthesia for cesarean delivery in the era of artificial intelligence: a narrative review.
Authors
Affiliations (8)
Affiliations (8)
- Department of Scienze dell'Emergenza, Anestesiologiche e della Rianimazione, IRCCS Fondazione Policlinico A. Gemelli, Rome, Italy. [email protected].
- Department of Scienze dell'Emergenza, Anestesiologiche e della Rianimazione, IRCCS Fondazione Policlinico A. Gemelli, Rome, Italy. [email protected].
- Department of Precision-Regenerative Medicine and Jonic Area (DiMePRe-J), Section of Anesthesiology and Intensive Care Medicine, University A. Moro, Bari, Italy.
- Unit of Obstetrics and Gynecology, Department Interdisciplinary Medicine, University A. Moro, Bari, Italy.
- Department of Perioperative Medicine, General Hospital F. Miulli, Acquaviva Delle Fonti, Italy.
- Department of Critical Care and Perinatal Medicine, IRCCS Ospedale G. Gaslini, Genoa, Italy.
- Department of Anesthesiology, Pain Therapy and Intensive Care, INT IRCCS Fondazione G. Pascale, Naples, Italy.
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.
Abstract
The ongoing revolution in artificial intelligence (AI) is reshaping perioperative care, including obstetric anesthesia. This narrative review synthesizes major AI applications in cesarean delivery, the world's most common inpatient surgery. Integrating history, obstetric factors, physiological variables, and imaging, AI tools enhance preoperative evaluation (estimation of risks of difficult airway), prediction of adverse events, ultrasound spine evaluation for neuraxial procedure, and postpartum hemorrhage. Language models can bridge consent and education gaps, while improving detection and treatment of postoperative pain. Machine learning models improve hemodynamic management with prediction of spinal-induced hypotension, assisted fluid management, and vasopressor requirements, with reduction of hypotensive burden. Yet cesarean-specific evidence remains limited and heterogeneous, with uncertain effects on maternal-neonatal outcomes. While promising, AI cannot replace the expertise and clinical judgment of a trained obstetric anesthesiologist. It should, instead, be viewed as a valuable tool to facilitate and support our practice, and multicenter prospective trials are needed to guide implementation.