Evidence map›Paper›PMID 41508047›Full record

ReviewJournal of anesthesia, analgesia and critical care2026

Anesthesia for cesarean delivery in the era of artificial intelligence: a narrative review.

Luciano Frassanito, Nicoletta Filetici, Pasquale Raimondo, Antonio Malvasi, Angela Gaudiano, Alessia Peragine, Francesca Lombardi, Francesco Vassalli, Gilda Pasta, Elena Giovanna Bignami

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Luciano FrassanitoDepartment of Scienze dell'Emergenza, Anestesiologiche e della Rianimazione, IRCCS Fondazione Policlinico A. Gemelli, Rome, Italy. luciano.frassanito@policlinicogemelli.it.
Nicoletta FileticiDepartment of Scienze dell'Emergenza, Anestesiologiche e della Rianimazione, IRCCS Fondazione Policlinico A. Gemelli, Rome, Italy. nicolettafiletici@gmail.com.
Pasquale RaimondoDepartment of Precision-Regenerative Medicine and Jonic Area (DiMePRe-J), Section of Anesthesiology and Intensive Care Medicine, University A. Moro, Bari, Italy.
Antonio MalvasiUnit of Obstetrics and Gynecology, Department Interdisciplinary Medicine, University A. Moro, Bari, Italy.
Angela GaudianoDepartment of Precision-Regenerative Medicine and Jonic Area (DiMePRe-J), Section of Anesthesiology and Intensive Care Medicine, University A. Moro, Bari, Italy.
Alessia PeragineDepartment of Precision-Regenerative Medicine and Jonic Area (DiMePRe-J), Section of Anesthesiology and Intensive Care Medicine, University A. Moro, Bari, Italy.
Francesca LombardiDepartment of Perioperative Medicine, General Hospital F. Miulli, Acquaviva Delle Fonti, Italy.
Francesco VassalliDepartment of Critical Care and Perinatal Medicine, IRCCS Ospedale G. Gaslini, Genoa, Italy.
Gilda PastaDepartment of Anesthesiology, Pain Therapy and Intensive Care, INT IRCCS Fondazione G. Pascale, Naples, Italy.
Elena Giovanna BignamiAnesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AnesthesiaArtificial intelligenceCesarean delivery

Identifiers

PMID41508047
PMCPMC12882529

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.