ReviewJournal of anesthesia, analgesia and critical care2026
Anesthesia for cesarean delivery in the era of artificial intelligence: a narrative review.
Review in Journal of anesthesia, analgesia and critical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.