The use of generative artificial intelligence in surgical education: a narrative review

Abstract

The introduction of generative artificial intelligence (AI) has revolutionized healthcare and education. These AI systems, trained on vast datasets using advanced machine learning (ML) techniques and large language models (LLMs), can generate text, images, and videos, offering new avenues for enhancing surgical education. Their ability to produce interactive learning resources, procedural guidance, and feedback post-virtual simulations makes them valuable in educating surgical trainees. However, technical challenges such as data quality issues, inaccuracies, and uncertainties around model interpretability remain barriers to widespread adoption. This review explores the integration of generative AI into surgical training, assessing its potential to enhance learning and teaching methodologies. While generative AI has demonstrated promise for improving surgical education, its integration must be approached cautiously, ensuring AI input is balanced with traditional supervision and mentorship from experienced surgeons. Given that generative AI models are not yet suitable as standalone tools, a blended learning approach that integrates AI capabilities with conventional educational strategies should be adopted. The review also addresses limitations and challenges, emphasizing the need for more robust research on different AI models and their applications across various surgical subspecialties. The lack of standardized frameworks and tools to assess the quality of AI outputs in surgical education necessitates rigorous oversight to ensure accuracy and reliability in training settings. By evaluating the current state of generative AI in surgical education, this narrative review highlights the potential for future innovation and research, encouraging ongoing exploration of AI in enhancing surgical education and training.

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