Trends in the Use of Artificial Intelligence in Surgical Simulation

Technical skill is a significant determinant of postoperative outcomes, yet traditional approaches to teaching and assessing surgical performance are resource-intensive, subjective, and challenging to scale. Simulation-based training was introduced to address these limitations; however, most programs still rely on human observation and expert availability. Artificial intelligence (AI) has recently emerged as a powerful complement to simulation, offering automated performance analysis, personalized feedback, and large-scale educational analytics. This narrative review describes current trends in the use of AI in surgical simulation. We first summarize innovations in machine learning and deep learning, natural language processing, and skills and performance analysis. We then outline key educational opportunities related to technical skills training, assessment, debriefing, and quality improvement. Implementation strategies and challenges are discussed, including institutional buy-in, funding and infrastructure, integration into existing curricula, and faculty development. Finally, we highlight future directions such as multimodal foundation models, generative simulation, global expansion into low and middle-income settings, and research priorities for rigorous validation and equity. Across multiple studies, AI-based systems can accurately classify expertise, segment procedures, and predict scores on validated rating scales. Randomized trials also show that AI tutoring can match or exceed expert instruction in simulated tasks. However, unintended effects, algorithmic bias, and limited evidence of long-term educational impact underscore the need for thoughtful implementation and sustained human oversight. AI, at least over the next 5 years, should be understood as an augmenting technology that can enhance, but not replace, expert surgical educators.