From prediction to practice: closing the translation gap in artificial intelligence for anesthesia

Artificial intelligence (AI) and machine learning (ML) techniques are rapidly advancing in anesthesiology, showing promise in patient monitoring, outcome prediction, clinical decision support, and automated drug delivery. However, a substantial gap remains between algorithmic capability and practical implementation at the bedside. This narrative review examines the current state of AI/ML applications in anesthesia, including predictive analytics, closed-loop control systems, AI-assisted imaging, workflow optimization, and anesthesia planning, and explores the translational barriers that have limited routine clinical adoption. We discuss technical, organizational, regulatory, and cultural challenges impeding translation, including data quality issues, EHR interoperability constraints, lack of outcome-oriented clinical evidence, business model uncertainty, interpretability concerns, alarm fatigue, and regulatory ambiguity. Strategies to close this gap are proposed, including rigorous prospective validation, interdisciplinary collaboration with industry and payers, post-deployment model surveillance, training data transparency, user-centered design, and implementation science principles. Ethical and legal considerations, encompassing algorithmic bias, accountability for autonomous AI recommendations, privacy beyond de-identification, and equitable access, are also reviewed. A conceptual framework, summary table of applications, and practical implementation checklist are provided. Bridging the translational divide is essential for AI to fulfill its potential in improving anesthesia care, and will require coordinated action from clinicians, researchers, technologists, regulators, and healthcare institutions.