Scoping Review Protocol: Artificial Intelligence in Anesthesia Education v1

Abstract

Introduction: Artificial intelligence (AI) is quickly changing anesthesia practice and education, with uses that include intelligent tutoring systems for airway management and predictive analytics for perioperative risk evaluation. AI-enabled simulators create realistic training environments where anesthesia residents can practice complex procedures safely, without risking patients. Incorporating AI into anesthesia education shows great promise for improving learning results, enhancing diagnostic accuracy training, and offering personalized educational experiences that suit each learner's needs. Purpose: This scoping review aims to systematically chart current AI applications in anesthesia medical education, identify key educational outcomes and effectiveness metrics, and explore the challenges and opportunities for implementing AI in anesthesia training programs. Method: A scoping review will follow the Arksey and O'Malley framework and JBI methodology. Studies published in English between 2020 and 2025 will be included. The literature search will cover MEDLINE, EMBASE, IEEE Xplore, ACM Digital Library, and educational databases. This review will use the six-step framework: identifying research questions, finding relevant studies, selecting studies, charting data, collating and summarizing results, and consulting stakeholders. Research Questions: What AI applications are currently used in anesthesia medical education? What educational outcomes and effectiveness measures are reported for AI in anesthesia training? What are the implementation challenges and facilitators for AI integration in anesthesia education programs? How do AI applications address competency-based training requirements in anesthesia?

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