Objective:

To assess existing evidence and the translational capabilities of AI in obstetric anesthesiology.

Approach:

Key Findings:

• AI has potential in predicting complications such as preeclampsia and postpartum hemorrhage.
• Current applications include machine learning-enhanced ultrasound for neuraxial interventions, predicting spinal anesthesia-induced hypotension, and anticipating maternal fever associated with epidurals and pain following cesarean delivery.
• Most research is retrospective and lacks external validation.
• Challenges include data variability, algorithmic bias, and difficulties in clinical integration.

Interpretation:

Limitations:

• Retrospective nature of most studies.
• Lack of external validation for AI models.
• Variability in data and algorithmic bias.

Conclusion:

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