Integrating Artificial Intelligence in Anesthesia Practice in Saudi Arabia

Abstract

Artificial intelligence (AI) is increasingly being explored to support anesthesiology across perioperative monitoring, prediction of adverse events, closed-loop drug delivery, airway management, and postoperative pain control. This narrative review synthesises current evidence on anesthesia-relevant AI and discusses implications for adoption in Saudi Arabia, integrating findings from major biomedical databases alongside national policy and governance considerations. Across the literature, AI systems are most commonly positioned as clinical decision-support tools intended to augment (not replace) anesthesiologists by improving early risk stratification, supporting individualized dosing, and enhancing vigilance for physiologic deterioration. Available Saudi survey evidence indicates strong professional receptivity; in one national survey, 73.39% of anesthesiologists reported that AI could be incorporated into perioperative practice, including depth-of-anesthesia monitoring, medication titration, airway risk prediction, and postoperative pain management. Despite this favourable attitude, real-world implementation remains limited. Recurrent barriers include uncertainty regarding data privacy and cybersecurity, potential algorithmic bias and poor generalizability across patient subgroups, limited transparency and explainability, unclear liability and accountability in adverse outcomes, and gaps in clinician training on AI capabilities, limitations, and regulatory requirements. Operational constraints are also prominent, including variable data quality, incomplete interoperability with electronic health records, and limited access to multidisciplinary teams to validate, deploy, and monitor models over time. Overall, the evidence suggests a persistent mismatch between clinician enthusiasm and system-level readiness. Closing this gap will require robust governance frameworks, context-specific validation in Saudi populations, continuous performance monitoring, and targeted workforce development aligned with national digital health transformation priorities. Prospective studies should evaluate safety, equity, and cost-effectiveness.