To highlight recent work and progress of AI in obstetric anaesthesia.
Approach:
Literature Review: A narrative review of the literature was conducted to summarize evidence regarding artificial intelligence use within obstetric anaesthesia, utilizing electronic databases and manual screening of reference lists.
Key Findings:
AI applications in obstetric anaesthesia include preoperative counselling, labour analgesia advice, perioperative risk stratification, and optimizing neuraxial techniques.
AI chatbots demonstrated high accuracy (97.9%) in preoperative management but require further validation in multicentre studies.
Deep learning models for airway risk prediction showed potential but have limitations in generalizability and clinical adoption.
Interpretation:
AI has the potential to enhance patient care in obstetric anaesthesia but faces challenges such as transparency, accuracy, and integration into clinical practice.
Limitations:
Lack of integration of dynamic intrapartum obstetric variables within AI models.
Limited external validation in prospective multicentre trials.
Generalizability issues due to studies being conducted at single institutions or with homogenous populations.
Conclusion:
Multidisciplinary collaboration is essential for the successful integration of AI in obstetric anaesthesia.
A large BRFSS analysis points to persistent screening disparities among sexual orientation and gender identity minority respondents, with particularly large gaps in some gender identity minority groups.
